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Kalman filter compilation cleanup (#1080) 

* start cleanup

* create generated dir if not exist

* tests pass!

* everything works again

* also convert live_kf to new structure

* Remove sympy helpers from file list

* Add laika to docker container

* Only build models that are present
pull/1084/head
Willem Melching 2020-02-12 09:40:28 -08:00 committed by GitHub
parent a790892796
commit 47fd50ca60
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GPG Key ID: 4AEE18F83AFDEB23
28 changed files with 1454 additions and 1448 deletions
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2
Dockerfile.openpilot
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@ -81,6 +81,8 @@ COPY ./pyextra /tmp/openpilot/pyextra
COPY ./panda /tmp/openpilot/panda
COPY ./external /tmp/openpilot/external
COPY ./tools /tmp/openpilot/tools
COPY ./laika /tmp/openpilot/laika
COPY ./laika_repo /tmp/openpilot/laika_repo
COPY SConstruct /tmp/openpilot/SConstruct

1
SConstruct
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@ -228,5 +228,6 @@ if arch == "aarch64":
SConscript(['selfdrive/clocksd/SConscript'])
SConscript(['selfdrive/locationd/SConscript'])
SConscript(['selfdrive/locationd/kalman/SConscript'])
# TODO: finish cereal, dbcbuilder, MPC

1
release/files_common
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@ -28,7 +28,6 @@ common/profiler.py
common/testing.py
common/basedir.py
common/filter_simple.py
common/sympy_helpers.py
common/stat_live.py
common/spinner.py
common/cython_hacks.py

6
selfdrive/locationd/kalman/.gitignore vendored
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@ -1,5 +1 @@
lane.cpp
gnss.cpp
loc*.cpp
live.cpp
pos_computer*.cpp
generated/

30
selfdrive/locationd/kalman/SConscript 100644
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@ -0,0 +1,30 @@
Import('env')
templates = Glob('templates/*')
sympy_helpers = "helpers/sympy_helpers.py"
ekf_sym = "helpers/ekf_sym.py"
to_build = {
'pos_computer_4': 'helpers/lst_sq_computer.py',
'feature_handler_5': 'helpers/feature_handler.py',
'gnss': 'models/gnss_kf.py',
'loc_4': 'models/loc_kf.py',
'live': 'models/live_kf.py',
'lane': '#xx/pipeline/lib/ekf/lane_kf.py',
}
found = {}
for target, command in to_build.items():
if File(command).exists():
found[target] = command
for target, command in found.items():
target_files = File([f'generated/{target}.cpp', f'generated/{target}.h'])
command_file = File(command)
env.Command(target_files,
[templates, command_file, sympy_helpers, ekf_sym],
command_file.get_abspath()
)
env.SharedLibrary('generated/' + target, target_files[0])

323
selfdrive/locationd/kalman/feature_handler.py
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@ -1,323 +0,0 @@
import common.transformations.orientation as orient
import numpy as np
import scipy.optimize as opt
import time
import os
from bisect import bisect_left
from common.sympy_helpers import sympy_into_c, quat_matrix_l
from common.ffi_wrapper import ffi_wrap, wrap_compiled, compile_code
EXTERNAL_PATH = os.path.dirname(os.path.abspath(__file__))
def sane(track):
img_pos = track[1:,2:4]
diffs_x = abs(img_pos[1:,0] - img_pos[:-1,0])
diffs_y = abs(img_pos[1:,1] - img_pos[:-1,1])
for i in range(1, len(diffs_x)):
if ((diffs_x[i] > 0.05 or diffs_x[i-1] > 0.05) and \
(diffs_x[i] > 2*diffs_x[i-1] or \
diffs_x[i] < .5*diffs_x[i-1])) or \
((diffs_y[i] > 0.05 or diffs_y[i-1] > 0.05) and \
(diffs_y[i] > 2*diffs_y[i-1] or \
diffs_y[i] < .5*diffs_y[i-1])):
return False
return True
class FeatureHandler():
def __init__(self, K):
self.MAX_TRACKS=6000
self.K = K
#Array of tracks, each track
#has K 5D features preceded
#by 5 params that inidicate
#[f_idx, last_idx, updated, complete, valid]
# f_idx: idx of current last feature in track
# idx of of last feature in frame
# bool for whether this track has been update
# bool for whether this track is complete
# bool for whether this track is valid
self.tracks = np.zeros((self.MAX_TRACKS, K+1, 5))
self.tracks[:] = np.nan
# Wrap c code for slow matching
c_header = "\nvoid merge_features(double *tracks, double *features, long long *empty_idxs);"
c_code = "#define K %d\n" % K
c_code += "\n" + open(os.path.join(EXTERNAL_PATH, "feature_handler.c")).read()
ffi, lib = ffi_wrap('feature_handler', c_code, c_header)
def merge_features_c(tracks, features, empty_idxs):
lib.merge_features(ffi.cast("double *", tracks.ctypes.data),
ffi.cast("double *", features.ctypes.data),
ffi.cast("long long *", empty_idxs.ctypes.data))
#self.merge_features = self.merge_features_python
self.merge_features = merge_features_c
def reset(self):
self.tracks[:] = np.nan
def merge_features_python(self, tracks, features, empty_idxs):
empty_idx = 0
for f in features:
match_idx = int(f[4])
if tracks[match_idx, 0, 1] == match_idx and tracks[match_idx, 0 ,2] == 0:
tracks[match_idx, 0, 0] += 1
tracks[match_idx, 0, 1] = f[1]
tracks[match_idx, 0, 2] = 1
tracks[match_idx, int(tracks[match_idx, 0, 0])] = f
if tracks[match_idx, 0, 0] == self.K:
tracks[match_idx, 0, 3] = 1
if sane(tracks[match_idx]):
tracks[match_idx, 0, 4] = 1
else:
if empty_idx == len(empty_idxs):
print('need more empty space')
continue
tracks[empty_idxs[empty_idx], 0, 0] = 1
tracks[empty_idxs[empty_idx], 0, 1] = f[1]
tracks[empty_idxs[empty_idx], 0, 2] = 1
tracks[empty_idxs[empty_idx], 1] = f
empty_idx += 1
def update_tracks(self, features):
t0 = time.time()
last_idxs = np.copy(self.tracks[:,0,1])
real = np.isfinite(last_idxs)
self.tracks[last_idxs[real].astype(int)] = self.tracks[real]
mask = np.ones(self.MAX_TRACKS, np.bool)
mask[last_idxs[real].astype(int)] = 0
empty_idxs = np.arange(self.MAX_TRACKS)[mask]
self.tracks[empty_idxs] = np.nan
self.tracks[:,0,2] = 0
self.merge_features(self.tracks, features, empty_idxs)
def handle_features(self, features):
self.update_tracks(features)
valid_idxs = self.tracks[:,0,4] == 1
complete_idxs = self.tracks[:,0,3] == 1
stale_idxs = self.tracks[:,0,2] == 0
valid_tracks = self.tracks[valid_idxs]
self.tracks[complete_idxs] = np.nan
self.tracks[stale_idxs] = np.nan
return valid_tracks[:,1:,:4].reshape((len(valid_tracks), self.K*4))
def generate_residual(K):
import sympy as sp
from common.sympy_helpers import quat_rotate
x_sym = sp.MatrixSymbol('abr', 3,1)
poses_sym = sp.MatrixSymbol('poses', 7*K,1)
img_pos_sym = sp.MatrixSymbol('img_positions', 2*K,1)
alpha, beta, rho = x_sym
to_c = sp.Matrix(orient.rot_matrix(-np.pi/2, -np.pi/2, 0))
pos_0 = sp.Matrix(np.array(poses_sym[K*7-7:K*7-4])[:,0])
q = poses_sym[K*7-4:K*7]
quat_rot = quat_rotate(*q)
rot_g_to_0 = to_c*quat_rot.T
rows = []
for i in range(K):
pos_i = sp.Matrix(np.array(poses_sym[i*7:i*7+3])[:,0])
q = poses_sym[7*i+3:7*i+7]
quat_rot = quat_rotate(*q)
rot_g_to_i = to_c*quat_rot.T
rot_0_to_i = rot_g_to_i*(rot_g_to_0.T)
trans_0_to_i = rot_g_to_i*(pos_0 - pos_i)
funct_vec = rot_0_to_i*sp.Matrix([alpha, beta, 1]) + rho*trans_0_to_i
h1, h2, h3 = funct_vec
rows.append(h1/h3 - img_pos_sym[i*2 +0])
rows.append(h2/h3 - img_pos_sym[i*2 + 1])
img_pos_residual_sym = sp.Matrix(rows)
# sympy into c
sympy_functions = []
sympy_functions.append(('res_fun', img_pos_residual_sym, [x_sym, poses_sym, img_pos_sym]))
sympy_functions.append(('jac_fun', img_pos_residual_sym.jacobian(x_sym), [x_sym, poses_sym, img_pos_sym]))
return sympy_functions
def generate_orient_error_jac(K):
import sympy as sp
from common.sympy_helpers import quat_rotate
x_sym = sp.MatrixSymbol('abr', 3,1)
dtheta = sp.MatrixSymbol('dtheta', 3,1)
delta_quat = sp.Matrix(np.ones(4))
delta_quat[1:,:] = sp.Matrix(0.5*dtheta[0:3,:])
poses_sym = sp.MatrixSymbol('poses', 7*K,1)
img_pos_sym = sp.MatrixSymbol('img_positions', 2*K,1)
alpha, beta, rho = x_sym
to_c = sp.Matrix(orient.rot_matrix(-np.pi/2, -np.pi/2, 0))
pos_0 = sp.Matrix(np.array(poses_sym[K*7-7:K*7-4])[:,0])
q = quat_matrix_l(poses_sym[K*7-4:K*7])*delta_quat
quat_rot = quat_rotate(*q)
rot_g_to_0 = to_c*quat_rot.T
rows = []
for i in range(K):
pos_i = sp.Matrix(np.array(poses_sym[i*7:i*7+3])[:,0])
q = quat_matrix_l(poses_sym[7*i+3:7*i+7])*delta_quat
quat_rot = quat_rotate(*q)
rot_g_to_i = to_c*quat_rot.T
rot_0_to_i = rot_g_to_i*(rot_g_to_0.T)
trans_0_to_i = rot_g_to_i*(pos_0 - pos_i)
funct_vec = rot_0_to_i*sp.Matrix([alpha, beta, 1]) + rho*trans_0_to_i
h1, h2, h3 = funct_vec
rows.append(h1/h3 - img_pos_sym[i*2 +0])
rows.append(h2/h3 - img_pos_sym[i*2 + 1])
img_pos_residual_sym = sp.Matrix(rows)
# sympy into c
sympy_functions = []
sympy_functions.append(('orient_error_jac', img_pos_residual_sym.jacobian(dtheta), [x_sym, poses_sym, img_pos_sym, dtheta]))
return sympy_functions
class LstSqComputer():
def __init__(self, K, MIN_DEPTH=2, MAX_DEPTH=500, debug=False):
self.to_c = orient.rot_matrix(-np.pi/2, -np.pi/2, 0)
self.MAX_DEPTH = MAX_DEPTH
self.MIN_DEPTH = MIN_DEPTH
self.debug = debug
self.name = 'pos_computer_' + str(K)
if debug:
self.name += '_debug'
try:
dir_path = os.path.dirname(__file__)
deps = [dir_path + '/' + 'feature_handler.py',
dir_path + '/' + 'compute_pos.c']
outs = [dir_path + '/' + self.name + '.o',
dir_path + '/' + self.name + '.so',
dir_path + '/' + self.name + '.cpp']
out_times = list(map(os.path.getmtime, outs))
dep_times = list(map(os.path.getmtime, deps))
rebuild = os.getenv("REBUILD", False)
if min(out_times) < max(dep_times) or rebuild:
list(map(os.remove, outs))
# raise the OSError if removing didnt
# raise one to start the compilation
raise OSError()
except OSError as e:
# gen c code for sympy functions
sympy_functions = generate_residual(K)
#if debug:
# sympy_functions.extend(generate_orient_error_jac(K))
header, code = sympy_into_c(sympy_functions)
# ffi wrap c code
extra_header = "\nvoid compute_pos(double *to_c, double *in_poses, double *in_img_positions, double *param, double *pos);"
code += "\n#define KDIM %d\n" % K
header += "\n" + extra_header
code += "\n" + open(os.path.join(EXTERNAL_PATH, "compute_pos.c")).read()
compile_code(self.name, code, header, EXTERNAL_PATH)
ffi, lib = wrap_compiled(self.name, EXTERNAL_PATH)
# wrap c functions
#if debug:
#def orient_error_jac(x, poses, img_positions, dtheta):
# out = np.zeros(((K*2, 3)), dtype=np.float64)
# lib.orient_error_jac(ffi.cast("double *", x.ctypes.data),
# ffi.cast("double *", poses.ctypes.data),
# ffi.cast("double *", img_positions.ctypes.data),
# ffi.cast("double *", dtheta.ctypes.data),
# ffi.cast("double *", out.ctypes.data))
# return out
#self.orient_error_jac = orient_error_jac
def residual_jac(x, poses, img_positions):
out = np.zeros(((K*2, 3)), dtype=np.float64)
lib.jac_fun(ffi.cast("double *", x.ctypes.data),
ffi.cast("double *", poses.ctypes.data),
ffi.cast("double *", img_positions.ctypes.data),
ffi.cast("double *", out.ctypes.data))
return out
def residual(x, poses, img_positions):
out = np.zeros((K*2), dtype=np.float64)
lib.res_fun(ffi.cast("double *", x.ctypes.data),
ffi.cast("double *", poses.ctypes.data),
ffi.cast("double *", img_positions.ctypes.data),
ffi.cast("double *", out.ctypes.data))
return out
self.residual = residual
self.residual_jac = residual_jac
def compute_pos_c(poses, img_positions):
pos = np.zeros(3, dtype=np.float64)
param = np.zeros(3, dtype=np.float64)
# Can't be a view for the ctype
img_positions = np.copy(img_positions)
lib.compute_pos(ffi.cast("double *", self.to_c.ctypes.data),
ffi.cast("double *", poses.ctypes.data),
ffi.cast("double *", img_positions.ctypes.data),
ffi.cast("double *", param.ctypes.data),
ffi.cast("double *", pos.ctypes.data))
return pos, param
self.compute_pos_c = compute_pos_c
def compute_pos(self, poses, img_positions, debug=False):
pos, param = self.compute_pos_c(poses, img_positions)
#pos, param = self.compute_pos_python(poses, img_positions)
depth = 1/param[2]
if debug:
if not self.debug:
raise NotImplementedError("This is not a debug computer")
#orient_err_jac = self.orient_error_jac(param, poses, img_positions, np.zeros(3)).reshape((-1,2,3))
jac = self.residual_jac(param, poses, img_positions).reshape((-1,2,3))
res = self.residual(param, poses, img_positions).reshape((-1,2))
return pos, param, res, jac #, orient_err_jac
elif (self.MIN_DEPTH < depth < self.MAX_DEPTH):
return pos
else:
return None
def gauss_newton(self, fun, jac, x, args):
poses, img_positions = args
delta = 1
counter = 0
while abs(np.linalg.norm(delta)) > 1e-4 and counter < 30:
delta = np.linalg.pinv(jac(x, poses, img_positions)).dot(fun(x, poses, img_positions))
x = x - delta
counter += 1
return [x]
def compute_pos_python(self, poses, img_positions, check_quality=False):
# This procedure is also described
# in the MSCKF paper (Mourikis et al. 2007)
x = np.array([img_positions[-1][0],
img_positions[-1][1], 0.1])
res = opt.leastsq(self.residual, x, Dfun=self.residual_jac, args=(poses, img_positions)) # scipy opt
#res = self.gauss_newton(self.residual, self.residual_jac, x, (poses, img_positions)) # diy gauss_newton
alpha, beta, rho = res[0]
rot_0_to_g = (orient.rotations_from_quats(poses[-1,3:])).dot(self.to_c.T)
return (rot_0_to_g.dot(np.array([alpha, beta, 1])))/rho + poses[-1,:3]
'''
EXPERIMENTAL CODE
'''
def unroll_shutter(img_positions, poses, v, rot_rates, ecef_pos):
# only speed correction for now
t_roll = 0.016 # 16ms rolling shutter?
vroll, vpitch, vyaw = rot_rates
A = 0.5*np.array([[-1, -vroll, -vpitch, -vyaw],
[vroll, 0, vyaw, -vpitch],
[vpitch, -vyaw, 0, vroll],
[vyaw, vpitch, -vroll, 0]])
q_dot = A.dot(poses[-1][3:7])
v = np.append(v, q_dot)
v = np.array([v[0], v[1], v[2],0,0,0,0])
current_pose = poses[-1] + v*0.05
poses = np.vstack((current_pose, poses))
dt = -img_positions[:,1]*t_roll/0.48
errs = project(poses, ecef_pos) - project(poses + np.atleast_2d(dt).T.dot(np.atleast_2d(v)), ecef_pos)
return img_positions - errs
def project(poses, ecef_pos):
img_positions = np.zeros((len(poses), 2))
for i, p in enumerate(poses):
cam_frame = orient.rotations_from_quats(p[3:]).T.dot(ecef_pos - p[:3])
img_positions[i] = np.array([cam_frame[1]/cam_frame[0], cam_frame[2]/cam_frame[0]])
return img_positions

105
selfdrive/locationd/kalman/gnss_kf.py
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@ -1,105 +0,0 @@
#!/usr/bin/env python3
import numpy as np
from . import gnss_model
from .kalman_helpers import ObservationKind
from .ekf_sym import EKF_sym
from selfdrive.locationd.kalman.loc_kf import parse_pr, parse_prr
class States():
ECEF_POS = slice(0,3) # x, y and z in ECEF in meters
ECEF_VELOCITY = slice(3,6)
CLOCK_BIAS = slice(6, 7) # clock bias in light-meters,
CLOCK_DRIFT = slice(7, 8) # clock drift in light-meters/s,
CLOCK_ACCELERATION = slice(8, 9) # clock acceleration in light-meters/s**2
GLONASS_BIAS = slice(9, 10) # clock drift in light-meters/s,
GLONASS_FREQ_SLOPE = slice(10, 11) # GLONASS bias in m expressed as bias + freq_num*freq_slope
class GNSSKalman():
def __init__(self, N=0, max_tracks=3000):
x_initial = np.array([-2712700.6008, -4281600.6679, 3859300.1830,
0, 0, 0,
0, 0, 0,
0, 0])
# state covariance
P_initial = np.diag([10000**2, 10000**2, 10000**2,
10**2, 10**2, 10**2,
(2000000)**2, (100)**2, (0.5)**2,
(10)**2, (1)**2])
# process noise
Q = np.diag([0.3**2, 0.3**2, 0.3**2,
3**2, 3**2, 3**2,
(.1)**2, (0)**2, (0.01)**2,
.1**2, (.01)**2])
self.dim_state = x_initial.shape[0]
# mahalanobis outlier rejection
maha_test_kinds = []#ObservationKind.PSEUDORANGE_RATE, ObservationKind.PSEUDORANGE, ObservationKind.PSEUDORANGE_GLONASS]
name = 'gnss'
gnss_model.gen_model(name, self.dim_state, maha_test_kinds)
# init filter
self.filter = EKF_sym(name, Q, x_initial, P_initial, self.dim_state, self.dim_state, maha_test_kinds=maha_test_kinds)
@property
def x(self):
return self.filter.state()
@property
def P(self):
return self.filter.covs()
def predict(self, t):
return self.filter.predict(t)
def rts_smooth(self, estimates):
return self.filter.rts_smooth(estimates, norm_quats=False)
def init_state(self, state, covs_diag=None, covs=None, filter_time=None):
if covs_diag is not None:
P = np.diag(covs_diag)
elif covs is not None:
P = covs
else:
P = self.filter.covs()
self.filter.init_state(state, P, filter_time)
def predict_and_observe(self, t, kind, data):
if len(data) > 0:
data = np.atleast_2d(data)
if kind == ObservationKind.PSEUDORANGE_GPS or kind == ObservationKind.PSEUDORANGE_GLONASS:
r = self.predict_and_update_pseudorange(data, t, kind)
elif kind == ObservationKind.PSEUDORANGE_RATE_GPS or kind == ObservationKind.PSEUDORANGE_RATE_GLONASS:
r = self.predict_and_update_pseudorange_rate(data, t, kind)
return r
def predict_and_update_pseudorange(self, meas, t, kind):
R = np.zeros((len(meas), 1, 1))
sat_pos_freq = np.zeros((len(meas), 4))
z = np.zeros((len(meas), 1))
for i, m in enumerate(meas):
z_i, R_i, sat_pos_freq_i = parse_pr(m)
sat_pos_freq[i,:] = sat_pos_freq_i
z[i,:] = z_i
R[i,:,:] = R_i
return self.filter.predict_and_update_batch(t, kind, z, R, sat_pos_freq)
def predict_and_update_pseudorange_rate(self, meas, t, kind):
R = np.zeros((len(meas), 1, 1))
z = np.zeros((len(meas), 1))
sat_pos_vel = np.zeros((len(meas), 6))
for i, m in enumerate(meas):
z_i, R_i, sat_pos_vel_i = parse_prr(m)
sat_pos_vel[i] = sat_pos_vel_i
R[i,:,:] = R_i
z[i, :] = z_i
return self.filter.predict_and_update_batch(t, kind, z, R, sat_pos_vel)
if __name__ == "__main__":
GNSSKalman()

93
selfdrive/locationd/kalman/gnss_model.py
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@ -1,93 +0,0 @@
import numpy as np
import sympy as sp
import os
from .kalman_helpers import ObservationKind
from .ekf_sym import gen_code
from common.sympy_helpers import cross, euler_rotate, quat_rotate, quat_matrix_l, quat_matrix_r
def gen_model(name, dim_state, maha_test_kinds):
# check if rebuild is needed
try:
dir_path = os.path.dirname(__file__)
deps = [dir_path + '/' + 'ekf_c.c',
dir_path + '/' + 'ekf_sym.py',
dir_path + '/' + 'gnss_model.py',
dir_path + '/' + 'gnss_kf.py']
outs = [dir_path + '/' + name + '.o',
dir_path + '/' + name + '.so',
dir_path + '/' + name + '.cpp']
out_times = list(map(os.path.getmtime, outs))
dep_times = list(map(os.path.getmtime, deps))
rebuild = os.getenv("REBUILD", False)
if min(out_times) > max(dep_times) and not rebuild:
return
list(map(os.remove, outs))
except OSError as e:
pass
# make functions and jacobians with sympy
# state variables
state_sym = sp.MatrixSymbol('state', dim_state, 1)
state = sp.Matrix(state_sym)
x,y,z = state[0:3,:]
v = state[3:6,:]
vx, vy, vz = v
cb, cd, ca = state[6:9,:]
glonass_bias, glonass_freq_slope = state[9:11,:]
dt = sp.Symbol('dt')
state_dot = sp.Matrix(np.zeros((dim_state, 1)))
state_dot[:3,:] = v
state_dot[6,0] = cd
state_dot[7,0] = ca
# Basic descretization, 1st order intergrator
# Can be pretty bad if dt is big
f_sym = state + dt*state_dot
#
# Observation functions
#
# extra args
sat_pos_freq_sym = sp.MatrixSymbol('sat_pos', 4, 1)
sat_pos_vel_sym = sp.MatrixSymbol('sat_pos_vel', 6, 1)
sat_los_sym = sp.MatrixSymbol('sat_los', 3, 1)
orb_epos_sym = sp.MatrixSymbol('orb_epos_sym', 3, 1)
# expand extra args
sat_x, sat_y, sat_z, glonass_freq = sat_pos_freq_sym
sat_vx, sat_vy, sat_vz = sat_pos_vel_sym[3:]
los_x, los_y, los_z = sat_los_sym
orb_x, orb_y, orb_z = orb_epos_sym
h_pseudorange_sym = sp.Matrix([sp.sqrt(
(x - sat_x)**2 +
(y - sat_y)**2 +
(z - sat_z)**2) +
cb])
h_pseudorange_glonass_sym = sp.Matrix([sp.sqrt(
(x - sat_x)**2 +
(y - sat_y)**2 +
(z - sat_z)**2) +
cb + glonass_bias + glonass_freq_slope*glonass_freq])
los_vector = (sp.Matrix(sat_pos_vel_sym[0:3]) - sp.Matrix([x, y, z]))
los_vector = los_vector / sp.sqrt(los_vector[0]**2 + los_vector[1]**2 + los_vector[2]**2)
h_pseudorange_rate_sym = sp.Matrix([los_vector[0]*(sat_vx - vx) +
los_vector[1]*(sat_vy - vy) +
los_vector[2]*(sat_vz - vz) +
cd])
obs_eqs = [[h_pseudorange_sym, ObservationKind.PSEUDORANGE_GPS, sat_pos_freq_sym],
[h_pseudorange_glonass_sym, ObservationKind.PSEUDORANGE_GLONASS, sat_pos_freq_sym],
[h_pseudorange_rate_sym, ObservationKind.PSEUDORANGE_RATE_GPS, sat_pos_vel_sym],
[h_pseudorange_rate_sym, ObservationKind.PSEUDORANGE_RATE_GLONASS, sat_pos_vel_sym]]
gen_code(name, f_sym, dt, state_sym, obs_eqs, dim_state, dim_state, maha_test_kinds=maha_test_kinds)

22
selfdrive/locationd/kalman/kalman_helpers.py → selfdrive/locationd/kalman/helpers/__init__.py
View File

@ -2,6 +2,28 @@ import numpy as np
import os
from bisect import bisect
from tqdm import tqdm
from cffi import FFI
TEMPLATE_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'templates'))
GENERATED_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'generated'))
def write_code(name, code, header):
if not os.path.exists(GENERATED_DIR):
os.mkdir(GENERATED_DIR)
open(os.path.join(GENERATED_DIR, f"{name}.cpp"), 'w').write(code)
open(os.path.join(GENERATED_DIR, f"{name}.h"), 'w').write(header)
def load_code(name):
shared_fn = os.path.join(GENERATED_DIR, f"lib{name}.so")
header_fn = os.path.join(GENERATED_DIR, f"{name}.h")
header = open(header_fn).read()
ffi = FFI()
ffi.cdef(header)
return (ffi, ffi.dlopen(shared_fn))
class KalmanError(Exception):

0
selfdrive/locationd/kalman/chi2_lookup.py → selfdrive/locationd/kalman/helpers/chi2_lookup.py
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0
selfdrive/locationd/kalman/chi2_lookup_table.npy → selfdrive/locationd/kalman/helpers/chi2_lookup_table.npy
View File

29
selfdrive/locationd/kalman/ekf_sym.py → selfdrive/locationd/kalman/helpers/ekf_sym.py
View File

@ -1,14 +1,16 @@
import os
from bisect import bisect_right
import sympy as sp
import numpy as np
import sympy as sp
from numpy import dot
from common.ffi_wrapper import compile_code, wrap_compiled
from common.sympy_helpers import sympy_into_c
from .chi2_lookup import chi2_ppf
from selfdrive.locationd.kalman.helpers.sympy_helpers import sympy_into_c
from selfdrive.locationd.kalman.helpers import (TEMPLATE_DIR, load_code,
write_code)
from selfdrive.locationd.kalman.helpers.chi2_lookup import chi2_ppf
EXTERNAL_PATH = os.path.dirname(os.path.abspath(__file__))
def solve(a, b):
if a.shape[0] == 1 and a.shape[1] == 1:
@ -17,6 +19,7 @@ def solve(a, b):
else:
return np.linalg.solve(a, b)
def null(H, eps=1e-12):
u, s, vh = np.linalg.svd(H)
padding = max(0,np.shape(H)[1]-np.shape(s)[0])
@ -24,6 +27,7 @@ def null(H, eps=1e-12):
null_space = np.compress(null_mask, vh, axis=0)
return np.transpose(null_space)
def gen_code(name, f_sym, dt_sym, x_sym, obs_eqs, dim_x, dim_err, eskf_params=None, msckf_params=None, maha_test_kinds=[]):
# optional state transition matrix, H modifier
# and err_function if an error-state kalman filter (ESKF)
@ -129,11 +133,13 @@ def gen_code(name, f_sym, dt_sym, x_sym, obs_eqs, dim_x, dim_err, eskf_params=No
extra_header += "\nconst static double MAHA_THRESH_%d = %f;" % (kind, maha_thresh)
extra_header += "\nvoid update_%d(double *, double *, double *, double *, double *);" % kind
code += "\n" + extra_header
code += "\n" + open(os.path.join(EXTERNAL_PATH, "ekf_c.c")).read()
code += "\n" + extra_post
code += '\nextern "C"{\n' + extra_header + "\n}\n"
code += "\n" + open(os.path.join(TEMPLATE_DIR, "ekf_c.c")).read()
code += '\nextern "C"{\n' + extra_post + "\n}\n"
header += "\n" + extra_header
compile_code(name, code, header, EXTERNAL_PATH)
write_code(name, code, header)
class EKF_sym():
def __init__(self, name, Q, x_initial, P_initial, dim_main, dim_main_err,
@ -174,7 +180,7 @@ class EKF_sym():
self.rewind_obscache = []
self.init_state(x_initial, P_initial, None)
ffi, lib = wrap_compiled(name, EXTERNAL_PATH)
ffi, lib = load_code(name)
kinds, self.feature_track_kinds = [], []
for func in dir(lib):
if func[:2] == 'h_':
@ -517,9 +523,6 @@ class EKF_sym():
else:
return True
def rts_smooth(self, estimates, norm_quats=False):
'''
Returns rts smoothed results of

159
selfdrive/locationd/kalman/helpers/feature_handler.py 100755
View File

@ -0,0 +1,159 @@
#!/usr/bin/env python3
import os
import time
import numpy as np
import common.transformations.orientation as orient
from selfdrive.locationd.kalman.helpers.sympy_helpers import quat_matrix_l
from selfdrive.locationd.kalman.helpers import TEMPLATE_DIR, write_code, load_code
def sane(track):
img_pos = track[1:,2:4]
diffs_x = abs(img_pos[1:,0] - img_pos[:-1,0])
diffs_y = abs(img_pos[1:,1] - img_pos[:-1,1])
for i in range(1, len(diffs_x)):
if ((diffs_x[i] > 0.05 or diffs_x[i-1] > 0.05) and \
(diffs_x[i] > 2*diffs_x[i-1] or \
diffs_x[i] < .5*diffs_x[i-1])) or \
((diffs_y[i] > 0.05 or diffs_y[i-1] > 0.05) and \
(diffs_y[i] > 2*diffs_y[i-1] or \
diffs_y[i] < .5*diffs_y[i-1])):
return False
return True
class FeatureHandler():
name = 'feature_handler'
@staticmethod
def generate_code(K=5):
# Wrap c code for slow matching
c_header = "\nvoid merge_features(double *tracks, double *features, long long *empty_idxs);"
c_code = "#include <math.h>\n"
c_code += "#include <string.h>\n"
c_code += "#define K %d\n" % K
c_code += "\n" + open(os.path.join(TEMPLATE_DIR, "feature_handler.c")).read()
filename = f"{FeatureHandler.name}_{K}"
write_code(filename, c_code, c_header)
def __init__(self, K=5):
self.MAX_TRACKS = 6000
self.K = K
#Array of tracks, each track
#has K 5D features preceded
#by 5 params that inidicate
#[f_idx, last_idx, updated, complete, valid]
# f_idx: idx of current last feature in track
# idx of of last feature in frame
# bool for whether this track has been update
# bool for whether this track is complete
# bool for whether this track is valid
self.tracks = np.zeros((self.MAX_TRACKS, K+1, 5))
self.tracks[:] = np.nan
name = f"{FeatureHandler.name}_{K}"
ffi, lib = load_code(name)
def merge_features_c(tracks, features, empty_idxs):
lib.merge_features(ffi.cast("double *", tracks.ctypes.data),
ffi.cast("double *", features.ctypes.data),
ffi.cast("long long *", empty_idxs.ctypes.data))
#self.merge_features = self.merge_features_python
self.merge_features = merge_features_c
def reset(self):
self.tracks[:] = np.nan
def merge_features_python(self, tracks, features, empty_idxs):
empty_idx = 0
for f in features:
match_idx = int(f[4])
if tracks[match_idx, 0, 1] == match_idx and tracks[match_idx, 0 ,2] == 0:
tracks[match_idx, 0, 0] += 1
tracks[match_idx, 0, 1] = f[1]
tracks[match_idx, 0, 2] = 1
tracks[match_idx, int(tracks[match_idx, 0, 0])] = f
if tracks[match_idx, 0, 0] == self.K:
tracks[match_idx, 0, 3] = 1
if sane(tracks[match_idx]):
tracks[match_idx, 0, 4] = 1
else:
if empty_idx == len(empty_idxs):
print('need more empty space')
continue
tracks[empty_idxs[empty_idx], 0, 0] = 1
tracks[empty_idxs[empty_idx], 0, 1] = f[1]
tracks[empty_idxs[empty_idx], 0, 2] = 1
tracks[empty_idxs[empty_idx], 1] = f
empty_idx += 1
def update_tracks(self, features):
t0 = time.time()
last_idxs = np.copy(self.tracks[:,0,1])
real = np.isfinite(last_idxs)
self.tracks[last_idxs[real].astype(int)] = self.tracks[real]
mask = np.ones(self.MAX_TRACKS, np.bool)
mask[last_idxs[real].astype(int)] = 0
empty_idxs = np.arange(self.MAX_TRACKS)[mask]
self.tracks[empty_idxs] = np.nan
self.tracks[:,0,2] = 0
self.merge_features(self.tracks, features, empty_idxs)
def handle_features(self, features):
self.update_tracks(features)
valid_idxs = self.tracks[:,0,4] == 1
complete_idxs = self.tracks[:,0,3] == 1
stale_idxs = self.tracks[:,0,2] == 0
valid_tracks = self.tracks[valid_idxs]
self.tracks[complete_idxs] = np.nan
self.tracks[stale_idxs] = np.nan
return valid_tracks[:,1:,:4].reshape((len(valid_tracks), self.K*4))
def generate_orient_error_jac(K):
import sympy as sp
from common.sympy_helpers import quat_rotate
x_sym = sp.MatrixSymbol('abr', 3,1)
dtheta = sp.MatrixSymbol('dtheta', 3,1)
delta_quat = sp.Matrix(np.ones(4))
delta_quat[1:,:] = sp.Matrix(0.5*dtheta[0:3,:])
poses_sym = sp.MatrixSymbol('poses', 7*K,1)
img_pos_sym = sp.MatrixSymbol('img_positions', 2*K,1)
alpha, beta, rho = x_sym
to_c = sp.Matrix(orient.rot_matrix(-np.pi/2, -np.pi/2, 0))
pos_0 = sp.Matrix(np.array(poses_sym[K*7-7:K*7-4])[:,0])
q = quat_matrix_l(poses_sym[K*7-4:K*7])*delta_quat
quat_rot = quat_rotate(*q)
rot_g_to_0 = to_c*quat_rot.T
rows = []
for i in range(K):
pos_i = sp.Matrix(np.array(poses_sym[i*7:i*7+3])[:,0])
q = quat_matrix_l(poses_sym[7*i+3:7*i+7])*delta_quat
quat_rot = quat_rotate(*q)
rot_g_to_i = to_c*quat_rot.T
rot_0_to_i = rot_g_to_i*(rot_g_to_0.T)
trans_0_to_i = rot_g_to_i*(pos_0 - pos_i)
funct_vec = rot_0_to_i*sp.Matrix([alpha, beta, 1]) + rho*trans_0_to_i
h1, h2, h3 = funct_vec
rows.append(h1/h3 - img_pos_sym[i*2 +0])
rows.append(h2/h3 - img_pos_sym[i*2 + 1])
img_pos_residual_sym = sp.Matrix(rows)
# sympy into c
sympy_functions = []
sympy_functions.append(('orient_error_jac', img_pos_residual_sym.jacobian(dtheta), [x_sym, poses_sym, img_pos_sym, dtheta]))
return sympy_functions
if __name__ == "__main__":
# TODO: get K from argparse
FeatureHandler.generate_code()

177
selfdrive/locationd/kalman/helpers/lst_sq_computer.py 100755
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@ -0,0 +1,177 @@
#!/usr/bin/env python3
import os
import numpy as np
import scipy.optimize as opt
import sympy as sp
import common.transformations.orientation as orient
from selfdrive.locationd.kalman.helpers import (TEMPLATE_DIR, load_code,
write_code)
from selfdrive.locationd.kalman.helpers.sympy_helpers import (quat_rotate,
sympy_into_c)
def generate_residual(K):
x_sym = sp.MatrixSymbol('abr', 3,1)
poses_sym = sp.MatrixSymbol('poses', 7*K,1)
img_pos_sym = sp.MatrixSymbol('img_positions', 2*K,1)
alpha, beta, rho = x_sym
to_c = sp.Matrix(orient.rot_matrix(-np.pi/2, -np.pi/2, 0))
pos_0 = sp.Matrix(np.array(poses_sym[K*7-7:K*7-4])[:,0])
q = poses_sym[K*7-4:K*7]
quat_rot = quat_rotate(*q)
rot_g_to_0 = to_c*quat_rot.T
rows = []
for i in range(K):
pos_i = sp.Matrix(np.array(poses_sym[i*7:i*7+3])[:,0])
q = poses_sym[7*i+3:7*i+7]
quat_rot = quat_rotate(*q)
rot_g_to_i = to_c*quat_rot.T
rot_0_to_i = rot_g_to_i*(rot_g_to_0.T)
trans_0_to_i = rot_g_to_i*(pos_0 - pos_i)
funct_vec = rot_0_to_i*sp.Matrix([alpha, beta, 1]) + rho*trans_0_to_i
h1, h2, h3 = funct_vec
rows.append(h1/h3 - img_pos_sym[i*2 +0])
rows.append(h2/h3 - img_pos_sym[i*2 + 1])
img_pos_residual_sym = sp.Matrix(rows)
# sympy into c
sympy_functions = []
sympy_functions.append(('res_fun', img_pos_residual_sym, [x_sym, poses_sym, img_pos_sym]))
sympy_functions.append(('jac_fun', img_pos_residual_sym.jacobian(x_sym), [x_sym, poses_sym, img_pos_sym]))
return sympy_functions
class LstSqComputer():
name = 'pos_computer'
@staticmethod
def generate_code(K=4):
sympy_functions = generate_residual(K)
header, code = sympy_into_c(sympy_functions)
code += "\n#define KDIM %d\n" % K
code += "\n" + open(os.path.join(TEMPLATE_DIR, "compute_pos.c")).read()
header += """
void compute_pos(double *to_c, double *in_poses, double *in_img_positions, double *param, double *pos);
"""
filename = f"{LstSqComputer.name}_{K}"
write_code(filename, code, header)
def __init__(self, K=4, MIN_DEPTH=2, MAX_DEPTH=500):
self.to_c = orient.rot_matrix(-np.pi/2, -np.pi/2, 0)
self.MAX_DEPTH = MAX_DEPTH
self.MIN_DEPTH = MIN_DEPTH
name = f"{LstSqComputer.name}_{K}"
ffi, lib = load_code(name)
# wrap c functions
def residual_jac(x, poses, img_positions):
out = np.zeros(((K*2, 3)), dtype=np.float64)
lib.jac_fun(ffi.cast("double *", x.ctypes.data),
ffi.cast("double *", poses.ctypes.data),
ffi.cast("double *", img_positions.ctypes.data),
ffi.cast("double *", out.ctypes.data))
return out
self.residual_jac = residual_jac
def residual(x, poses, img_positions):
out = np.zeros((K*2), dtype=np.float64)
lib.res_fun(ffi.cast("double *", x.ctypes.data),
ffi.cast("double *", poses.ctypes.data),
ffi.cast("double *", img_positions.ctypes.data),
ffi.cast("double *", out.ctypes.data))
return out
self.residual = residual
def compute_pos_c(poses, img_positions):
pos = np.zeros(3, dtype=np.float64)
param = np.zeros(3, dtype=np.float64)
# Can't be a view for the ctype
img_positions = np.copy(img_positions)
lib.compute_pos(ffi.cast("double *", self.to_c.ctypes.data),
ffi.cast("double *", poses.ctypes.data),
ffi.cast("double *", img_positions.ctypes.data),
ffi.cast("double *", param.ctypes.data),
ffi.cast("double *", pos.ctypes.data))
return pos, param
self.compute_pos_c = compute_pos_c
def compute_pos(self, poses, img_positions, debug=False):
pos, param = self.compute_pos_c(poses, img_positions)
#pos, param = self.compute_pos_python(poses, img_positions)
depth = 1/param[2]
if debug:
if not self.debug:
raise NotImplementedError("This is not a debug computer")
#orient_err_jac = self.orient_error_jac(param, poses, img_positions, np.zeros(3)).reshape((-1,2,3))
jac = self.residual_jac(param, poses, img_positions).reshape((-1,2,3))
res = self.residual(param, poses, img_positions).reshape((-1,2))
return pos, param, res, jac #, orient_err_jac
elif (self.MIN_DEPTH < depth < self.MAX_DEPTH):
return pos
else:
return None
def gauss_newton(self, fun, jac, x, args):
poses, img_positions = args
delta = 1
counter = 0
while abs(np.linalg.norm(delta)) > 1e-4 and counter < 30:
delta = np.linalg.pinv(jac(x, poses, img_positions)).dot(fun(x, poses, img_positions))
x = x - delta
counter += 1
return [x]
def compute_pos_python(self, poses, img_positions, check_quality=False):
# This procedure is also described
# in the MSCKF paper (Mourikis et al. 2007)
x = np.array([img_positions[-1][0],
img_positions[-1][1], 0.1])
res = opt.leastsq(self.residual, x, Dfun=self.residual_jac, args=(poses, img_positions)) # scipy opt
#res = self.gauss_newton(self.residual, self.residual_jac, x, (poses, img_positions)) # diy gauss_newton
alpha, beta, rho = res[0]
rot_0_to_g = (orient.rotations_from_quats(poses[-1,3:])).dot(self.to_c.T)
return (rot_0_to_g.dot(np.array([alpha, beta, 1])))/rho + poses[-1,:3]
'''
EXPERIMENTAL CODE
'''
def unroll_shutter(img_positions, poses, v, rot_rates, ecef_pos):
# only speed correction for now
t_roll = 0.016 # 16ms rolling shutter?
vroll, vpitch, vyaw = rot_rates
A = 0.5*np.array([[-1, -vroll, -vpitch, -vyaw],
[vroll, 0, vyaw, -vpitch],
[vpitch, -vyaw, 0, vroll],
[vyaw, vpitch, -vroll, 0]])
q_dot = A.dot(poses[-1][3:7])
v = np.append(v, q_dot)
v = np.array([v[0], v[1], v[2],0,0,0,0])
current_pose = poses[-1] + v*0.05
poses = np.vstack((current_pose, poses))
dt = -img_positions[:,1]*t_roll/0.48
errs = project(poses, ecef_pos) - project(poses + np.atleast_2d(dt).T.dot(np.atleast_2d(v)), ecef_pos)
return img_positions - errs
def project(poses, ecef_pos):
img_positions = np.zeros((len(poses), 2))
for i, p in enumerate(poses):
cam_frame = orient.rotations_from_quats(p[3:]).T.dot(ecef_pos - p[:3])
img_positions[i] = np.array([cam_frame[1]/cam_frame[0], cam_frame[2]/cam_frame[0]])
return img_positions
if __name__ == "__main__":
# TODO: get K from argparse
LstSqComputer.generate_code()

2
common/sympy_helpers.py → selfdrive/locationd/kalman/helpers/sympy_helpers.py
View File

@ -78,4 +78,6 @@ def sympy_into_c(sympy_functions):
c_code = '\n'.join(x for x in c_code.split("\n") if len(x) > 0 and x[0] != '#')
c_header = '\n'.join(x for x in c_header.split("\n") if len(x) > 0 and x[0] != '#')
c_code = 'extern "C" {\n#include <math.h>\n' + c_code + "\n}\n"
return c_header, c_code

142
selfdrive/locationd/kalman/live_kf.py
View File

@ -1,142 +0,0 @@
#!/usr/bin/env python3
import numpy as np
from selfdrive.swaglog import cloudlog
from selfdrive.locationd.kalman.live_model import gen_model, States
from selfdrive.locationd.kalman.kalman_helpers import ObservationKind, KalmanError
from selfdrive.locationd.kalman.ekf_sym import EKF_sym
initial_x = np.array([-2.7e6, 4.2e6, 3.8e6,
1, 0, 0, 0,
0, 0, 0,
0, 0, 0,
0, 0, 0,
1,
0, 0, 0,
0, 0, 0])
# state covariance
initial_P_diag = np.array([10000**2, 10000**2, 10000**2,
10**2, 10**2, 10**2,
10**2, 10**2, 10**2,
1**2, 1**2, 1**2,
0.05**2, 0.05**2, 0.05**2,
0.02**2,
1**2, 1**2, 1**2,
(0.01)**2, (0.01)**2, (0.01)**2])
class LiveKalman():
def __init__(self):
# process noise
Q = np.diag([0.03**2, 0.03**2, 0.03**2,
0.0**2, 0.0**2, 0.0**2,
0.0**2, 0.0**2, 0.0**2,
0.1**2, 0.1**2, 0.1**2,
(0.005/100)**2, (0.005/100)**2, (0.005/100)**2,
(0.02/100)**2,
3**2, 3**2, 3**2,
(0.05/60)**2, (0.05/60)**2, (0.05/60)**2])
self.dim_state = initial_x.shape[0]
self.dim_state_err = initial_P_diag.shape[0]
self.obs_noise = {ObservationKind.ODOMETRIC_SPEED: np.atleast_2d(0.2**2),
ObservationKind.PHONE_GYRO: np.diag([0.025**2, 0.025**2, 0.025**2]),
ObservationKind.PHONE_ACCEL: np.diag([.5**2, .5**2, .5*2]),
ObservationKind.CAMERA_ODO_ROTATION: np.diag([0.05**2, 0.05**2, 0.05**2]),
ObservationKind.IMU_FRAME: np.diag([0.05**2, 0.05**2, 0.05**2]),
ObservationKind.NO_ROT: np.diag([0.00025**2, 0.00025**2, 0.00025**2]),
ObservationKind.ECEF_POS: np.diag([5**2, 5**2, 5**2])}
name = 'live'
gen_model(name, self.dim_state, self.dim_state_err, [])
# init filter
self.filter = EKF_sym(name, Q, initial_x, np.diag(initial_P_diag), self.dim_state, self.dim_state_err)
@property
def x(self):
return self.filter.state()
@property
def t(self):
return self.filter.filter_time
@property
def P(self):
return self.filter.covs()
def predict(self, t):
return self.filter.predict(t)
def rts_smooth(self, estimates):
return self.filter.rts_smooth(estimates, norm_quats=True)
def init_state(self, state, covs_diag=None, covs=None, filter_time=None):
if covs_diag is not None:
P = np.diag(covs_diag)
elif covs is not None:
P = covs
else:
P = self.filter.covs()
self.filter.init_state(state, P, filter_time)
def predict_and_observe(self, t, kind, data):
if len(data) > 0:
data = np.atleast_2d(data)
if kind == ObservationKind.CAMERA_ODO_TRANSLATION:
r = self.predict_and_update_odo_trans(data, t, kind)
elif kind == ObservationKind.CAMERA_ODO_ROTATION:
r = self.predict_and_update_odo_rot(data, t, kind)
elif kind == ObservationKind.ODOMETRIC_SPEED:
r = self.predict_and_update_odo_speed(data, t, kind)
else:
r = self.filter.predict_and_update_batch(t, kind, data, self.get_R(kind, len(data)))
# Normalize quats
quat_norm = np.linalg.norm(self.filter.x[3:7, 0])
# Should not continue if the quats behave this weirdly
if not (0.1 < quat_norm < 10):
cloudlog.error("Kalman filter quaternions unstable")
raise KalmanError
self.filter.x[States.ECEF_ORIENTATION, 0] = self.filter.x[States.ECEF_ORIENTATION, 0] / quat_norm
return r
def get_R(self, kind, n):
obs_noise = self.obs_noise[kind]
dim = obs_noise.shape[0]
R = np.zeros((n, dim, dim))
for i in range(n):
R[i, :, :] = obs_noise
return R
def predict_and_update_odo_speed(self, speed, t, kind):
z = np.array(speed)
R = np.zeros((len(speed), 1, 1))
for i, _ in enumerate(z):
R[i, :, :] = np.diag([0.2**2])
return self.filter.predict_and_update_batch(t, kind, z, R)
def predict_and_update_odo_trans(self, trans, t, kind):
z = trans[:, :3]
R = np.zeros((len(trans), 3, 3))
for i, _ in enumerate(z):
R[i, :, :] = np.diag(trans[i, 3:]**2)
return self.filter.predict_and_update_batch(t, kind, z, R)
def predict_and_update_odo_rot(self, rot, t, kind):
z = rot[:, :3]
R = np.zeros((len(rot), 3, 3))
for i, _ in enumerate(z):
R[i, :, :] = np.diag(rot[i, 3:]**2)
return self.filter.predict_and_update_batch(t, kind, z, R)
if __name__ == "__main__":
LiveKalman()

178
selfdrive/locationd/kalman/live_model.py
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@ -1,178 +0,0 @@
import numpy as np
import sympy as sp
import os
import sysconfig
from laika.constants import EARTH_GM
from common.sympy_helpers import euler_rotate, quat_rotate, quat_matrix_r
from selfdrive.locationd.kalman.kalman_helpers import ObservationKind
from selfdrive.locationd.kalman.ekf_sym import gen_code
class States():
ECEF_POS = slice(0, 3) # x, y and z in ECEF in meters
ECEF_ORIENTATION = slice(3, 7) # quat for pose of phone in ecef
ECEF_VELOCITY = slice(7, 10) # ecef velocity in m/s
ANGULAR_VELOCITY = slice(10, 13) # roll, pitch and yaw rates in device frame in radians/s
GYRO_BIAS = slice(13, 16) # roll, pitch and yaw biases
ODO_SCALE = slice(16, 17) # odometer scale
ACCELERATION = slice(17, 20) # Acceleration in device frame in m/s**2
IMU_OFFSET = slice(20, 23) # imu offset angles in radians
ECEF_POS_ERR = slice(0, 3)
ECEF_ORIENTATION_ERR = slice(3, 6)
ECEF_VELOCITY_ERR = slice(6, 9)
ANGULAR_VELOCITY_ERR = slice(9, 12)
GYRO_BIAS_ERR = slice(12, 15)
ODO_SCALE_ERR = slice(15, 16)
ACCELERATION_ERR = slice(16, 19)
IMU_OFFSET_ERR = slice(19, 22)
def gen_model(name, dim_state, dim_state_err, maha_test_kinds):
# check if rebuild is needed
try:
dir_path = os.path.dirname(__file__)
deps = [dir_path + '/' + 'ekf_c.c',
dir_path + '/' + 'ekf_sym.py',
dir_path + '/' + name + '_model.py',
dir_path + '/' + name + '_kf.py']
outs = [dir_path + '/' + name + '.o',
dir_path + '/' + name + sysconfig.get_config_var('EXT_SUFFIX'),
dir_path + '/' + name + '.cpp']
out_times = list(map(os.path.getmtime, outs))
dep_times = list(map(os.path.getmtime, deps))
rebuild = os.getenv("REBUILD", False)
if min(out_times) > max(dep_times) and not rebuild:
return
list(map(os.remove, outs))
except OSError as e:
print('HAHAHA')
print(e)
pass
# make functions and jacobians with sympy
# state variables
state_sym = sp.MatrixSymbol('state', dim_state, 1)
state = sp.Matrix(state_sym)
x,y,z = state[States.ECEF_POS,:]
q = state[States.ECEF_ORIENTATION,:]
v = state[States.ECEF_VELOCITY,:]
vx, vy, vz = v
omega = state[States.ANGULAR_VELOCITY,:]
vroll, vpitch, vyaw = omega
roll_bias, pitch_bias, yaw_bias = state[States.GYRO_BIAS,:]
odo_scale = state[16,:]
acceleration = state[States.ACCELERATION,:]
imu_angles= state[States.IMU_OFFSET,:]
dt = sp.Symbol('dt')
# calibration and attitude rotation matrices
quat_rot = quat_rotate(*q)
# Got the quat predict equations from here
# A New Quaternion-Based Kalman Filter for
# Real-Time Attitude Estimation Using the Two-Step
# Geometrically-Intuitive Correction Algorithm
A = 0.5*sp.Matrix([[0, -vroll, -vpitch, -vyaw],
[vroll, 0, vyaw, -vpitch],
[vpitch, -vyaw, 0, vroll],
[vyaw, vpitch, -vroll, 0]])
q_dot = A * q
# Time derivative of the state as a function of state
state_dot = sp.Matrix(np.zeros((dim_state, 1)))
state_dot[States.ECEF_POS,:] = v
state_dot[States.ECEF_ORIENTATION,:] = q_dot
state_dot[States.ECEF_VELOCITY,0] = quat_rot * acceleration
# Basic descretization, 1st order intergrator
# Can be pretty bad if dt is big
f_sym = state + dt*state_dot
state_err_sym = sp.MatrixSymbol('state_err',dim_state_err,1)
state_err = sp.Matrix(state_err_sym)
quat_err = state_err[States.ECEF_ORIENTATION_ERR,:]
v_err = state_err[States.ECEF_VELOCITY_ERR,:]
omega_err = state_err[States.ANGULAR_VELOCITY_ERR,:]
acceleration_err = state_err[States.ACCELERATION_ERR,:]
# Time derivative of the state error as a function of state error and state
quat_err_matrix = euler_rotate(quat_err[0], quat_err[1], quat_err[2])
q_err_dot = quat_err_matrix * quat_rot * (omega + omega_err)
state_err_dot = sp.Matrix(np.zeros((dim_state_err, 1)))
state_err_dot[States.ECEF_POS_ERR,:] = v_err
state_err_dot[States.ECEF_ORIENTATION_ERR,:] = q_err_dot
state_err_dot[States.ECEF_VELOCITY_ERR,:] = quat_err_matrix * quat_rot * (acceleration + acceleration_err)
f_err_sym = state_err + dt*state_err_dot
# Observation matrix modifier
H_mod_sym = sp.Matrix(np.zeros((dim_state, dim_state_err)))
H_mod_sym[0:3, 0:3] = np.eye(3)
H_mod_sym[3:7,3:6] = 0.5*quat_matrix_r(state[3:7])[:,1:]
H_mod_sym[7:, 6:] = np.eye(dim_state-7)
# these error functions are defined so that say there
# is a nominal x and true x:
# true x = err_function(nominal x, delta x)
# delta x = inv_err_function(nominal x, true x)
nom_x = sp.MatrixSymbol('nom_x',dim_state,1)
true_x = sp.MatrixSymbol('true_x',dim_state,1)
delta_x = sp.MatrixSymbol('delta_x',dim_state_err,1)
err_function_sym = sp.Matrix(np.zeros((dim_state,1)))
delta_quat = sp.Matrix(np.ones((4)))
delta_quat[1:,:] = sp.Matrix(0.5*delta_x[3:6,:])
err_function_sym[0:3,:] = sp.Matrix(nom_x[0:3,:] + delta_x[0:3,:])
err_function_sym[3:7,0] = quat_matrix_r(nom_x[3:7,0])*delta_quat
err_function_sym[7:,:] = sp.Matrix(nom_x[7:,:] + delta_x[6:,:])
inv_err_function_sym = sp.Matrix(np.zeros((dim_state_err,1)))
inv_err_function_sym[0:3,0] = sp.Matrix(-nom_x[0:3,0] + true_x[0:3,0])
delta_quat = quat_matrix_r(nom_x[3:7,0]).T*true_x[3:7,0]
inv_err_function_sym[3:6,0] = sp.Matrix(2*delta_quat[1:])
inv_err_function_sym[6:,0] = sp.Matrix(-nom_x[7:,0] + true_x[7:,0])
eskf_params = [[err_function_sym, nom_x, delta_x],
[inv_err_function_sym, nom_x, true_x],
H_mod_sym, f_err_sym, state_err_sym]
#
# Observation functions
#
imu_rot = euler_rotate(*imu_angles)
h_gyro_sym = imu_rot*sp.Matrix([vroll + roll_bias,
vpitch + pitch_bias,
vyaw + yaw_bias])
pos = sp.Matrix([x, y, z])
gravity = quat_rot.T * ((EARTH_GM/((x**2 + y**2 + z**2)**(3.0/2.0)))*pos)
h_acc_sym = imu_rot*(gravity + acceleration)
h_phone_rot_sym = sp.Matrix([vroll,
vpitch,
vyaw])
speed = vx**2 + vy**2 + vz**2
h_speed_sym = sp.Matrix([sp.sqrt(speed)*odo_scale])
h_pos_sym = sp.Matrix([x, y, z])
h_imu_frame_sym = sp.Matrix(imu_angles)
h_relative_motion = sp.Matrix(quat_rot.T * v)
obs_eqs = [[h_speed_sym, ObservationKind.ODOMETRIC_SPEED, None],
[h_gyro_sym, ObservationKind.PHONE_GYRO, None],
[h_phone_rot_sym, ObservationKind.NO_ROT, None],
[h_acc_sym, ObservationKind.PHONE_ACCEL, None],
[h_pos_sym, ObservationKind.ECEF_POS, None],
[h_relative_motion, ObservationKind.CAMERA_ODO_TRANSLATION, None],
[h_phone_rot_sym, ObservationKind.CAMERA_ODO_ROTATION, None],
[h_imu_frame_sym, ObservationKind.IMU_FRAME, None]]
gen_code(name, f_sym, dt, state_sym, obs_eqs, dim_state, dim_state_err, eskf_params)

323
selfdrive/locationd/kalman/loc_kf.py
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#!/usr/bin/env python3
import numpy as np
from . import loc_model
from .kalman_helpers import ObservationKind
from .ekf_sym import EKF_sym
from .feature_handler import LstSqComputer, unroll_shutter
from laika.raw_gnss import GNSSMeasurement
def parse_prr(m):
sat_pos_vel_i = np.concatenate((m[GNSSMeasurement.SAT_POS],
m[GNSSMeasurement.SAT_VEL]))
R_i = np.atleast_2d(m[GNSSMeasurement.PRR_STD]**2)
z_i = m[GNSSMeasurement.PRR]
return z_i, R_i, sat_pos_vel_i
def parse_pr(m):
pseudorange = m[GNSSMeasurement.PR]
pseudorange_stdev = m[GNSSMeasurement.PR_STD]
sat_pos_freq_i = np.concatenate((m[GNSSMeasurement.SAT_POS],
np.array([m[GNSSMeasurement.GLONASS_FREQ]])))
z_i = np.atleast_1d(pseudorange)
R_i = np.atleast_2d(pseudorange_stdev**2)
return z_i, R_i, sat_pos_freq_i
class States():
ECEF_POS = slice(0,3) # x, y and z in ECEF in meters
ECEF_ORIENTATION = slice(3,7) # quat for pose of phone in ecef
ECEF_VELOCITY = slice(7,10) # ecef velocity in m/s
ANGULAR_VELOCITY = slice(10, 13) # roll, pitch and yaw rates in device frame in radians/s
CLOCK_BIAS = slice(13, 14) # clock bias in light-meters,
CLOCK_DRIFT = slice(14, 15) # clock drift in light-meters/s,
GYRO_BIAS = slice(15, 18) # roll, pitch and yaw biases
ODO_SCALE = slice(18, 19) # odometer scale
ACCELERATION = slice(19, 22) # Acceleration in device frame in m/s**2
FOCAL_SCALE = slice(22, 23) # focal length scale
IMU_OFFSET = slice(23,26) # imu offset angles in radians
GLONASS_BIAS = slice(26,27) # GLONASS bias in m expressed as bias + freq_num*freq_slope
GLONASS_FREQ_SLOPE = slice(27, 28) # GLONASS bias in m expressed as bias + freq_num*freq_slope
CLOCK_ACCELERATION = slice(28, 29) # clock acceleration in light-meters/s**2,
class LocKalman():
def __init__(self, N=0, max_tracks=3000):
x_initial = np.array([-2.7e6, 4.2e6, 3.8e6,
1, 0, 0, 0,
0, 0, 0,
0, 0, 0,
0, 0,
0, 0, 0,
1,
0, 0, 0,
1,
0, 0, 0,
0, 0,
0])
# state covariance
P_initial = np.diag([10000**2, 10000**2, 10000**2,
10**2, 10**2, 10**2,
10**2, 10**2, 10**2,
1**2, 1**2, 1**2,
(200000)**2, (100)**2,
0.05**2, 0.05**2, 0.05**2,
0.02**2,
1**2, 1**2, 1**2,
0.01**2,
(0.01)**2, (0.01)**2, (0.01)**2,
10**2, 1**2,
0.05**2])
# process noise
Q = np.diag([0.03**2, 0.03**2, 0.03**2,
0.0**2, 0.0**2, 0.0**2,
0.0**2, 0.0**2, 0.0**2,
0.1**2, 0.1**2, 0.1**2,
(.1)**2, (0.0)**2,
(0.005/100)**2, (0.005/100)**2, (0.005/100)**2,
(0.02/100)**2,
3**2, 3**2, 3**2,
0.001**2,
(0.05/60)**2, (0.05/60)**2, (0.05/60)**2,
(.1)**2, (.01)**2,
0.005**2])
self.obs_noise = {ObservationKind.ODOMETRIC_SPEED: np.atleast_2d(0.2**2),
ObservationKind.PHONE_GYRO: np.diag([0.025**2, 0.025**2, 0.025**2]),
ObservationKind.PHONE_ACCEL: np.diag([.5**2, .5**2, .5*2]),
ObservationKind.CAMERA_ODO_ROTATION: np.diag([0.05**2, 0.05**2, 0.05**2]),
ObservationKind.IMU_FRAME: np.diag([0.05**2, 0.05**2, 0.05**2]),
ObservationKind.NO_ROT: np.diag([0.00025**2, 0.00025**2, 0.00025**2]),
ObservationKind.ECEF_POS: np.diag([5**2, 5**2, 5**2])}
# MSCKF stuff
self.N = N
self.dim_main = x_initial.shape[0]
self.dim_augment = 7
self.dim_main_err = P_initial.shape[0]
self.dim_augment_err = 6
self.dim_state = self.dim_main + self.dim_augment*self.N
self.dim_state_err = self.dim_main_err + self.dim_augment_err*self.N
# mahalanobis outlier rejection
maha_test_kinds = [ObservationKind.ORB_FEATURES] #, ObservationKind.PSEUDORANGE, ObservationKind.PSEUDORANGE_RATE]
name = 'loc_%d' % N
loc_model.gen_model(name, N, self.dim_main, self.dim_main_err,
self.dim_augment, self.dim_augment_err,
self.dim_state, self.dim_state_err,
maha_test_kinds)
if self.N > 0:
x_initial, P_initial, Q = self.pad_augmented(x_initial, P_initial, Q)
self.computer = LstSqComputer(N)
self.max_tracks = max_tracks
# init filter
self.filter = EKF_sym(name, Q, x_initial, P_initial, self.dim_main, self.dim_main_err,
N, self.dim_augment, self.dim_augment_err, maha_test_kinds)
@property
def x(self):
return self.filter.state()
@property
def t(self):
return self.filter.filter_time
@property
def P(self):
return self.filter.covs()
def predict(self, t):
return self.filter.predict(t)
def rts_smooth(self, estimates):
return self.filter.rts_smooth(estimates, norm_quats=True)
def pad_augmented(self, x, P, Q=None):
if x.shape[0] == self.dim_main and self.N > 0:
x = np.pad(x, (0, self.N*self.dim_augment), mode='constant')
x[self.dim_main+3::7] = 1
if P.shape[0] == self.dim_main_err and self.N > 0:
P = np.pad(P, [(0, self.N*self.dim_augment_err), (0, self.N*self.dim_augment_err)], mode='constant')
P[self.dim_main_err:, self.dim_main_err:] = 10e20*np.eye(self.dim_augment_err *self.N)
if Q is None:
return x, P
else:
Q = np.pad(Q, [(0, self.N*self.dim_augment_err), (0, self.N*self.dim_augment_err)], mode='constant')
return x, P, Q
def init_state(self, state, covs_diag=None, covs=None, filter_time=None):
if covs_diag is not None:
P = np.diag(covs_diag)
elif covs is not None:
P = covs
else:
P = self.filter.covs()
state, P = self.pad_augmented(state, P)
self.filter.init_state(state, P, filter_time)
def predict_and_observe(self, t, kind, data):
if len(data) > 0:
data = np.atleast_2d(data)
if kind == ObservationKind.CAMERA_ODO_TRANSLATION:
r = self.predict_and_update_odo_trans(data, t, kind)
elif kind == ObservationKind.CAMERA_ODO_ROTATION:
r = self.predict_and_update_odo_rot(data, t, kind)
elif kind == ObservationKind.PSEUDORANGE_GPS or kind == ObservationKind.PSEUDORANGE_GLONASS:
r = self.predict_and_update_pseudorange(data, t, kind)
elif kind == ObservationKind.PSEUDORANGE_RATE_GPS or kind == ObservationKind.PSEUDORANGE_RATE_GLONASS:
r = self.predict_and_update_pseudorange_rate(data, t, kind)
elif kind == ObservationKind.ORB_POINT:
r = self.predict_and_update_orb(data, t, kind)
elif kind == ObservationKind.ORB_FEATURES:
r = self.predict_and_update_orb_features(data, t, kind)
elif kind == ObservationKind.MSCKF_TEST:
r = self.predict_and_update_msckf_test(data, t, kind)
elif kind == ObservationKind.FEATURE_TRACK_TEST:
r = self.predict_and_update_feature_track_test(data, t, kind)
elif kind == ObservationKind.ODOMETRIC_SPEED:
r = self.predict_and_update_odo_speed(data, t, kind)
else:
r = self.filter.predict_and_update_batch(t, kind, data, self.get_R(kind, len(data)))
# Normalize quats
quat_norm = np.linalg.norm(self.filter.x[3:7,0])
# Should not continue if the quats behave this weirdly
if not 0.1 < quat_norm < 10:
raise RuntimeError("Sir! The filter's gone all wobbly!")
self.filter.x[3:7,0] = self.filter.x[3:7,0]/quat_norm
for i in range(self.N):
d1 = self.dim_main
d3 = self.dim_augment
self.filter.x[d1+d3*i+3:d1+d3*i+7] /= np.linalg.norm(self.filter.x[d1+i*d3 + 3:d1+i*d3 + 7,0])
return r
def get_R(self, kind, n):
obs_noise = self.obs_noise[kind]
dim = obs_noise.shape[0]
R = np.zeros((n, dim, dim))
for i in range(n):
R[i,:,:] = obs_noise
return R
def predict_and_update_pseudorange(self, meas, t, kind):
R = np.zeros((len(meas), 1, 1))
sat_pos_freq = np.zeros((len(meas), 4))
z = np.zeros((len(meas), 1))
for i, m in enumerate(meas):
z_i, R_i, sat_pos_freq_i = parse_pr(m)
sat_pos_freq[i,:] = sat_pos_freq_i
z[i,:] = z_i
R[i,:,:] = R_i
return self.filter.predict_and_update_batch(t, kind, z, R, sat_pos_freq)
def predict_and_update_pseudorange_rate(self, meas, t, kind):
R = np.zeros((len(meas), 1, 1))
z = np.zeros((len(meas), 1))
sat_pos_vel = np.zeros((len(meas), 6))
for i, m in enumerate(meas):
z_i, R_i, sat_pos_vel_i = parse_prr(m)
sat_pos_vel[i] = sat_pos_vel_i
R[i,:,:] = R_i
z[i, :] = z_i
return self.filter.predict_and_update_batch(t, kind, z, R, sat_pos_vel)
def predict_and_update_orb(self, orb, t, kind):
true_pos = orb[:,2:]
z = orb[:,:2]
R = np.zeros((len(orb), 2, 2))
for i, _ in enumerate(z):
R[i,:,:] = np.diag([10**2, 10**2])
return self.filter.predict_and_update_batch(t, kind, z, R, true_pos)
def predict_and_update_odo_speed(self, speed, t, kind):
z = np.array(speed)
R = np.zeros((len(speed), 1, 1))
for i, _ in enumerate(z):
R[i,:,:] = np.diag([0.2**2])
return self.filter.predict_and_update_batch(t, kind, z, R)
def predict_and_update_odo_trans(self, trans, t, kind):
z = trans[:,:3]
R = np.zeros((len(trans), 3, 3))
for i, _ in enumerate(z):
R[i,:,:] = np.diag(trans[i,3:]**2)
return self.filter.predict_and_update_batch(t, kind, z, R)
def predict_and_update_odo_rot(self, rot, t, kind):
z = rot[:,:3]
R = np.zeros((len(rot), 3, 3))
for i, _ in enumerate(z):
R[i,:,:] = np.diag(rot[i,3:]**2)
return self.filter.predict_and_update_batch(t, kind, z, R)
def predict_and_update_orb_features(self, tracks, t, kind):
k = 2*(self.N+1)
R = np.zeros((len(tracks), k, k))
z = np.zeros((len(tracks), k))
ecef_pos = np.zeros((len(tracks), 3))
ecef_pos[:] = np.nan
poses = self.x[self.dim_main:].reshape((-1,7))
times = tracks.reshape((len(tracks),self.N+1, 4))[:,:,0]
good_counter = 0
if times.any() and np.allclose(times[0,:-1], self.filter.augment_times, rtol=1e-6):
for i, track in enumerate(tracks):
img_positions = track.reshape((self.N+1, 4))[:,2:]
# TODO not perfect as last pose not used
#img_positions = unroll_shutter(img_positions, poses, self.filter.state()[7:10], self.filter.state()[10:13], ecef_pos[i])
ecef_pos[i] = self.computer.compute_pos(poses, img_positions[:-1])
z[i] = img_positions.flatten()
R[i,:,:] = np.diag([0.005**2]*(k))
if np.isfinite(ecef_pos[i][0]):
good_counter += 1
if good_counter > self.max_tracks:
break
good_idxs = np.all(np.isfinite(ecef_pos),axis=1)
# have to do some weird stuff here to keep
# to have the observations input from mesh3d
# consistent with the outputs of the filter
# Probably should be replaced, not sure how.
ret = self.filter.predict_and_update_batch(t, kind, z[good_idxs], R[good_idxs], ecef_pos[good_idxs], augment=True)
if ret is None:
return
y_full = np.zeros((z.shape[0], z.shape[1] - 3))
#print sum(good_idxs), len(tracks)
if sum(good_idxs) > 0:
y_full[good_idxs] = np.array(ret[6])
ret = ret[:6] + (y_full, z, ecef_pos)
return ret
def predict_and_update_msckf_test(self, test_data, t, kind):
assert self.N > 0
z = test_data
R = np.zeros((len(test_data), len(z[0]), len(z[0])))
ecef_pos = [self.x[:3]]
for i, _ in enumerate(z):
R[i,:,:] = np.diag([0.1**2]*len(z[0]))
ret = self.filter.predict_and_update_batch(t, kind, z, R, ecef_pos)
self.filter.augment()
return ret
def maha_test_pseudorange(self, x, P, meas, kind, maha_thresh=.3):
bools = []
for i, m in enumerate(meas):
z, R, sat_pos_freq = parse_pr(m)
bools.append(self.filter.maha_test(x, P, kind, z, R, extra_args=sat_pos_freq, maha_thresh=maha_thresh))
return np.array(bools)
def maha_test_pseudorange_rate(self, x, P, meas, kind, maha_thresh=.999):
bools = []
for i, m in enumerate(meas):
z, R, sat_pos_vel = parse_prr(m)
bools.append(self.filter.maha_test(x, P, kind, z, R, extra_args=sat_pos_vel, maha_thresh=maha_thresh))
return np.array(bools)
if __name__ == "__main__":
LocKalman(N=4)

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selfdrive/locationd/kalman/loc_model.py
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import numpy as np
import sympy as sp
import os
from laika.constants import EARTH_GM
from .kalman_helpers import ObservationKind
from .ekf_sym import gen_code
from common.sympy_helpers import cross, euler_rotate, quat_rotate, quat_matrix_l, quat_matrix_r
def gen_model(name, N, dim_main, dim_main_err,
dim_augment, dim_augment_err,
dim_state, dim_state_err,
maha_test_kinds):
# check if rebuild is needed
try:
dir_path = os.path.dirname(__file__)
deps = [dir_path + '/' + 'ekf_c.c',
dir_path + '/' + 'ekf_sym.py',
dir_path + '/' + 'loc_model.py',
dir_path + '/' + 'loc_kf.py']
outs = [dir_path + '/' + name + '.o',
dir_path + '/' + name + '.so',
dir_path + '/' + name + '.cpp']
out_times = list(map(os.path.getmtime, outs))
dep_times = list(map(os.path.getmtime, deps))
rebuild = os.getenv("REBUILD", False)
if min(out_times) > max(dep_times) and not rebuild:
return
list(map(os.remove, outs))
except OSError as e:
pass
# make functions and jacobians with sympy
# state variables
state_sym = sp.MatrixSymbol('state', dim_state, 1)
state = sp.Matrix(state_sym)
x,y,z = state[0:3,:]
q = state[3:7,:]
v = state[7:10,:]
vx, vy, vz = v
omega = state[10:13,:]
vroll, vpitch, vyaw = omega
cb, cd = state[13:15,:]
roll_bias, pitch_bias, yaw_bias = state[15:18,:]
odo_scale = state[18,:]
acceleration = state[19:22,:]
focal_scale = state[22,:]
imu_angles= state[23:26,:]
glonass_bias, glonass_freq_slope = state[26:28,:]
ca = state[28,0]
dt = sp.Symbol('dt')
# calibration and attitude rotation matrices
quat_rot = quat_rotate(*q)
# Got the quat predict equations from here
# A New Quaternion-Based Kalman Filter for
# Real-Time Attitude Estimation Using the Two-Step
# Geometrically-Intuitive Correction Algorithm
A = 0.5*sp.Matrix([[0, -vroll, -vpitch, -vyaw],
[vroll, 0, vyaw, -vpitch],
[vpitch, -vyaw, 0, vroll],
[vyaw, vpitch, -vroll, 0]])
q_dot = A * q
# Time derivative of the state as a function of state
state_dot = sp.Matrix(np.zeros((dim_state, 1)))
state_dot[:3,:] = v
state_dot[3:7,:] = q_dot
state_dot[7:10,0] = quat_rot * acceleration
state_dot[13,0] = cd
state_dot[14,0] = ca
# Basic descretization, 1st order intergrator
# Can be pretty bad if dt is big
f_sym = state + dt*state_dot
state_err_sym = sp.MatrixSymbol('state_err',dim_state_err,1)
state_err = sp.Matrix(state_err_sym)
quat_err = state_err[3:6,:]
v_err = state_err[6:9,:]
omega_err = state_err[9:12,:]
cd_err = state_err[13,:]
acceleration_err = state_err[18:21,:]
ca_err = state_err[27,:]
# Time derivative of the state error as a function of state error and state
quat_err_matrix = euler_rotate(quat_err[0], quat_err[1], quat_err[2])
q_err_dot = quat_err_matrix * quat_rot * (omega + omega_err)
state_err_dot = sp.Matrix(np.zeros((dim_state_err, 1)))
state_err_dot[:3,:] = v_err
state_err_dot[3:6,:] = q_err_dot
state_err_dot[6:9,:] = quat_err_matrix * quat_rot * (acceleration + acceleration_err)
state_err_dot[12,:] = cd_err
state_err_dot[13,:] = ca_err
f_err_sym = state_err + dt*state_err_dot
# convenient indexing
# q idxs are for quats and p idxs are for other
q_idxs = [[3, dim_augment]] + [[dim_main + n*dim_augment + 3, dim_main + (n+1)*dim_augment] for n in range(N)]
q_err_idxs = [[3, dim_augment_err]] + [[dim_main_err + n*dim_augment_err + 3, dim_main_err + (n+1)*dim_augment_err] for n in range(N)]
p_idxs = [[0, 3]] + [[dim_augment, dim_main]] + [[dim_main + n*dim_augment , dim_main + n*dim_augment + 3] for n in range(N)]
p_err_idxs = [[0, 3]] + [[dim_augment_err, dim_main_err]] + [[dim_main_err + n*dim_augment_err, dim_main_err + n*dim_augment_err + 3] for n in range(N)]
# Observation matrix modifier
H_mod_sym = sp.Matrix(np.zeros((dim_state, dim_state_err)))
for p_idx, p_err_idx in zip(p_idxs, p_err_idxs):
H_mod_sym[p_idx[0]:p_idx[1],p_err_idx[0]:p_err_idx[1]] = np.eye(p_idx[1]-p_idx[0])
for q_idx, q_err_idx in zip(q_idxs, q_err_idxs):
H_mod_sym[q_idx[0]:q_idx[1],q_err_idx[0]:q_err_idx[1]] = 0.5*quat_matrix_r(state[q_idx[0]:q_idx[1]])[:,1:]
# these error functions are defined so that say there
# is a nominal x and true x:
# true x = err_function(nominal x, delta x)
# delta x = inv_err_function(nominal x, true x)
nom_x = sp.MatrixSymbol('nom_x',dim_state,1)
true_x = sp.MatrixSymbol('true_x',dim_state,1)
delta_x = sp.MatrixSymbol('delta_x',dim_state_err,1)
err_function_sym = sp.Matrix(np.zeros((dim_state,1)))
for q_idx, q_err_idx in zip(q_idxs, q_err_idxs):
delta_quat = sp.Matrix(np.ones((4)))
delta_quat[1:,:] = sp.Matrix(0.5*delta_x[q_err_idx[0]: q_err_idx[1],:])
err_function_sym[q_idx[0]:q_idx[1],0] = quat_matrix_r(nom_x[q_idx[0]:q_idx[1],0])*delta_quat
for p_idx, p_err_idx in zip(p_idxs, p_err_idxs):
err_function_sym[p_idx[0]:p_idx[1],:] = sp.Matrix(nom_x[p_idx[0]:p_idx[1],:] + delta_x[p_err_idx[0]:p_err_idx[1],:])
inv_err_function_sym = sp.Matrix(np.zeros((dim_state_err,1)))
for p_idx, p_err_idx in zip(p_idxs, p_err_idxs):
inv_err_function_sym[p_err_idx[0]:p_err_idx[1],0] = sp.Matrix(-nom_x[p_idx[0]:p_idx[1],0] + true_x[p_idx[0]:p_idx[1],0])
for q_idx, q_err_idx in zip(q_idxs, q_err_idxs):
delta_quat = quat_matrix_r(nom_x[q_idx[0]:q_idx[1],0]).T*true_x[q_idx[0]:q_idx[1],0]
inv_err_function_sym[q_err_idx[0]:q_err_idx[1],0] = sp.Matrix(2*delta_quat[1:])
eskf_params = [[err_function_sym, nom_x, delta_x],
[inv_err_function_sym, nom_x, true_x],
H_mod_sym, f_err_sym, state_err_sym]
#
# Observation functions
#
# extra args
sat_pos_freq_sym = sp.MatrixSymbol('sat_pos', 4, 1)
sat_pos_vel_sym = sp.MatrixSymbol('sat_pos_vel', 6, 1)
sat_los_sym = sp.MatrixSymbol('sat_los', 3, 1)
orb_epos_sym = sp.MatrixSymbol('orb_epos_sym', 3, 1)
# expand extra args
sat_x, sat_y, sat_z, glonass_freq = sat_pos_freq_sym
sat_vx, sat_vy, sat_vz = sat_pos_vel_sym[3:]
los_x, los_y, los_z = sat_los_sym
orb_x, orb_y, orb_z = orb_epos_sym
h_pseudorange_sym = sp.Matrix([sp.sqrt(
(x - sat_x)**2 +
(y - sat_y)**2 +
(z - sat_z)**2) +
cb])
h_pseudorange_glonass_sym = sp.Matrix([sp.sqrt(
(x - sat_x)**2 +
(y - sat_y)**2 +
(z - sat_z)**2) +
cb + glonass_bias + glonass_freq_slope*glonass_freq])
los_vector = (sp.Matrix(sat_pos_vel_sym[0:3]) - sp.Matrix([x, y, z]))
los_vector = los_vector / sp.sqrt(los_vector[0]**2 + los_vector[1]**2 + los_vector[2]**2)
h_pseudorange_rate_sym = sp.Matrix([los_vector[0]*(sat_vx - vx) +
los_vector[1]*(sat_vy - vy) +
los_vector[2]*(sat_vz - vz) +
cd])
imu_rot = euler_rotate(*imu_angles)
h_gyro_sym = imu_rot*sp.Matrix([vroll + roll_bias,
vpitch + pitch_bias,
vyaw + yaw_bias])
pos = sp.Matrix([x, y, z])
gravity = quat_rot.T * ((EARTH_GM/((x**2 + y**2 + z**2)**(3.0/2.0)))*pos)
h_acc_sym = imu_rot*(gravity + acceleration)
h_phone_rot_sym = sp.Matrix([vroll,
vpitch,
vyaw])
speed = vx**2 + vy**2 + vz**2
h_speed_sym = sp.Matrix([sp.sqrt(speed)*odo_scale])
# orb stuff
orb_pos_sym = sp.Matrix([orb_x - x, orb_y - y, orb_z - z])
orb_pos_rot_sym = quat_rot.T * orb_pos_sym
s = orb_pos_rot_sym[0]
h_orb_point_sym = sp.Matrix([(1/s)*(orb_pos_rot_sym[1]),
(1/s)*(orb_pos_rot_sym[2])])
h_pos_sym = sp.Matrix([x, y, z])
h_imu_frame_sym = sp.Matrix(imu_angles)
h_relative_motion = sp.Matrix(quat_rot.T * v)
obs_eqs = [[h_speed_sym, ObservationKind.ODOMETRIC_SPEED, None],
[h_gyro_sym, ObservationKind.PHONE_GYRO, None],
[h_phone_rot_sym, ObservationKind.NO_ROT, None],
[h_acc_sym, ObservationKind.PHONE_ACCEL, None],
[h_pseudorange_sym, ObservationKind.PSEUDORANGE_GPS, sat_pos_freq_sym],
[h_pseudorange_glonass_sym, ObservationKind.PSEUDORANGE_GLONASS, sat_pos_freq_sym],
[h_pseudorange_rate_sym, ObservationKind.PSEUDORANGE_RATE_GPS, sat_pos_vel_sym],
[h_pseudorange_rate_sym, ObservationKind.PSEUDORANGE_RATE_GLONASS, sat_pos_vel_sym],
[h_pos_sym, ObservationKind.ECEF_POS, None],
[h_relative_motion, ObservationKind.CAMERA_ODO_TRANSLATION, None],
[h_phone_rot_sym, ObservationKind.CAMERA_ODO_ROTATION, None],
[h_imu_frame_sym, ObservationKind.IMU_FRAME, None],
[h_orb_point_sym, ObservationKind.ORB_POINT, orb_epos_sym]]
# MSCKF configuration
if N > 0:
focal_scale =1
# Add observation functions for orb feature tracks
track_epos_sym = sp.MatrixSymbol('track_epos_sym', 3, 1)
track_x, track_y, track_z = track_epos_sym
h_track_sym = sp.Matrix(np.zeros(((1 + N)*2, 1)))
track_pos_sym = sp.Matrix([track_x - x, track_y - y, track_z - z])
track_pos_rot_sym = quat_rot.T * track_pos_sym
h_track_sym[-2:,:] = sp.Matrix([focal_scale*(track_pos_rot_sym[1]/track_pos_rot_sym[0]),
focal_scale*(track_pos_rot_sym[2]/track_pos_rot_sym[0])])
h_msckf_test_sym = sp.Matrix(np.zeros(((1 + N)*3, 1)))
h_msckf_test_sym[-3:,:] = sp.Matrix([track_x - x,track_y - y , track_z - z])
for n in range(N):
idx = dim_main + n*dim_augment
err_idx = dim_main_err + n*dim_augment_err
x, y, z = state[idx:idx+3]
q = state[idx+3:idx+7]
quat_rot = quat_rotate(*q)
track_pos_sym = sp.Matrix([track_x - x, track_y - y, track_z - z])
track_pos_rot_sym = quat_rot.T * track_pos_sym
h_track_sym[n*2:n*2+2,:] = sp.Matrix([focal_scale*(track_pos_rot_sym[1]/track_pos_rot_sym[0]),
focal_scale*(track_pos_rot_sym[2]/track_pos_rot_sym[0])])
h_msckf_test_sym[n*3:n*3+3,:] = sp.Matrix([track_x - x, track_y - y, track_z - z])
obs_eqs.append([h_msckf_test_sym, ObservationKind.MSCKF_TEST, track_epos_sym])
obs_eqs.append([h_track_sym, ObservationKind.ORB_FEATURES, track_epos_sym])
obs_eqs.append([h_track_sym, ObservationKind.FEATURE_TRACK_TEST, track_epos_sym])
msckf_params = [dim_main, dim_augment, dim_main_err, dim_augment_err, N, [ObservationKind.MSCKF_TEST, ObservationKind.ORB_FEATURES]]
else:
msckf_params = None
gen_code(name, f_sym, dt, state_sym, obs_eqs, dim_state, dim_state_err, eskf_params, msckf_params, maha_test_kinds)

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selfdrive/locationd/kalman/models/__init__.py 100644
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selfdrive/locationd/kalman/models/gnss_kf.py 100755
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#!/usr/bin/env python3
import numpy as np
import sympy as sp
from selfdrive.locationd.kalman.helpers import ObservationKind
from selfdrive.locationd.kalman.helpers.ekf_sym import EKF_sym, gen_code
from selfdrive.locationd.kalman.models.loc_kf import parse_pr, parse_prr
class States():
ECEF_POS = slice(0,3) # x, y and z in ECEF in meters
ECEF_VELOCITY = slice(3,6)
CLOCK_BIAS = slice(6, 7) # clock bias in light-meters,
CLOCK_DRIFT = slice(7, 8) # clock drift in light-meters/s,
CLOCK_ACCELERATION = slice(8, 9) # clock acceleration in light-meters/s**2
GLONASS_BIAS = slice(9, 10) # clock drift in light-meters/s,
GLONASS_FREQ_SLOPE = slice(10, 11) # GLONASS bias in m expressed as bias + freq_num*freq_slope
class GNSSKalman():
name = 'gnss'
x_initial = np.array([-2712700.6008, -4281600.6679, 3859300.1830,
0, 0, 0,
0, 0, 0,
0, 0])
# state covariance
P_initial = np.diag([10000**2, 10000**2, 10000**2,
10**2, 10**2, 10**2,
(2000000)**2, (100)**2, (0.5)**2,
(10)**2, (1)**2])
# process noise
Q = np.diag([0.3**2, 0.3**2, 0.3**2,
3**2, 3**2, 3**2,
(.1)**2, (0)**2, (0.01)**2,
.1**2, (.01)**2])
maha_test_kinds = [] #ObservationKind.PSEUDORANGE_RATE, ObservationKind.PSEUDORANGE, ObservationKind.PSEUDORANGE_GLONASS]
@staticmethod
def generate_code():
dim_state = GNSSKalman.x_initial.shape[0]
name = GNSSKalman.name
maha_test_kinds = GNSSKalman.maha_test_kinds
# make functions and jacobians with sympy
# state variables
state_sym = sp.MatrixSymbol('state', dim_state, 1)
state = sp.Matrix(state_sym)
x,y,z = state[0:3,:]
v = state[3:6,:]
vx, vy, vz = v
cb, cd, ca = state[6:9,:]
glonass_bias, glonass_freq_slope = state[9:11,:]
dt = sp.Symbol('dt')
state_dot = sp.Matrix(np.zeros((dim_state, 1)))
state_dot[:3,:] = v
state_dot[6,0] = cd
state_dot[7,0] = ca
# Basic descretization, 1st order integrator
# Can be pretty bad if dt is big
f_sym = state + dt*state_dot
#
# Observation functions
#
# extra args
sat_pos_freq_sym = sp.MatrixSymbol('sat_pos', 4, 1)
sat_pos_vel_sym = sp.MatrixSymbol('sat_pos_vel', 6, 1)
sat_los_sym = sp.MatrixSymbol('sat_los', 3, 1)
orb_epos_sym = sp.MatrixSymbol('orb_epos_sym', 3, 1)
# expand extra args
sat_x, sat_y, sat_z, glonass_freq = sat_pos_freq_sym
sat_vx, sat_vy, sat_vz = sat_pos_vel_sym[3:]
los_x, los_y, los_z = sat_los_sym
orb_x, orb_y, orb_z = orb_epos_sym
h_pseudorange_sym = sp.Matrix([sp.sqrt(
(x - sat_x)**2 +
(y - sat_y)**2 +
(z - sat_z)**2) +
cb])
h_pseudorange_glonass_sym = sp.Matrix([sp.sqrt(
(x - sat_x)**2 +
(y - sat_y)**2 +
(z - sat_z)**2) +
cb + glonass_bias + glonass_freq_slope*glonass_freq])
los_vector = (sp.Matrix(sat_pos_vel_sym[0:3]) - sp.Matrix([x, y, z]))
los_vector = los_vector / sp.sqrt(los_vector[0]**2 + los_vector[1]**2 + los_vector[2]**2)
h_pseudorange_rate_sym = sp.Matrix([los_vector[0]*(sat_vx - vx) +
los_vector[1]*(sat_vy - vy) +
los_vector[2]*(sat_vz - vz) +
cd])
obs_eqs = [[h_pseudorange_sym, ObservationKind.PSEUDORANGE_GPS, sat_pos_freq_sym],
[h_pseudorange_glonass_sym, ObservationKind.PSEUDORANGE_GLONASS, sat_pos_freq_sym],
[h_pseudorange_rate_sym, ObservationKind.PSEUDORANGE_RATE_GPS, sat_pos_vel_sym],
[h_pseudorange_rate_sym, ObservationKind.PSEUDORANGE_RATE_GLONASS, sat_pos_vel_sym]]
gen_code(name, f_sym, dt, state_sym, obs_eqs, dim_state, dim_state, maha_test_kinds=maha_test_kinds)
def __init__(self):
self.dim_state = self.x_initial.shape[0]
# init filter
self.filter = EKF_sym(self.name, self.Q, self.x_initial, self.P_initial, self.dim_state, self.dim_state, maha_test_kinds=self.maha_test_kinds)
@property
def x(self):
return self.filter.state()
@property
def P(self):
return self.filter.covs()
def predict(self, t):
return self.filter.predict(t)
def rts_smooth(self, estimates):
return self.filter.rts_smooth(estimates, norm_quats=False)
def init_state(self, state, covs_diag=None, covs=None, filter_time=None):
if covs_diag is not None:
P = np.diag(covs_diag)
elif covs is not None:
P = covs
else:
P = self.filter.covs()
self.filter.init_state(state, P, filter_time)
def predict_and_observe(self, t, kind, data):
if len(data) > 0:
data = np.atleast_2d(data)
if kind == ObservationKind.PSEUDORANGE_GPS or kind == ObservationKind.PSEUDORANGE_GLONASS:
r = self.predict_and_update_pseudorange(data, t, kind)
elif kind == ObservationKind.PSEUDORANGE_RATE_GPS or kind == ObservationKind.PSEUDORANGE_RATE_GLONASS:
r = self.predict_and_update_pseudorange_rate(data, t, kind)
return r
def predict_and_update_pseudorange(self, meas, t, kind):
R = np.zeros((len(meas), 1, 1))
sat_pos_freq = np.zeros((len(meas), 4))
z = np.zeros((len(meas), 1))
for i, m in enumerate(meas):
z_i, R_i, sat_pos_freq_i = parse_pr(m)
sat_pos_freq[i,:] = sat_pos_freq_i
z[i,:] = z_i
R[i,:,:] = R_i
return self.filter.predict_and_update_batch(t, kind, z, R, sat_pos_freq)
def predict_and_update_pseudorange_rate(self, meas, t, kind):
R = np.zeros((len(meas), 1, 1))
z = np.zeros((len(meas), 1))
sat_pos_vel = np.zeros((len(meas), 6))
for i, m in enumerate(meas):
z_i, R_i, sat_pos_vel_i = parse_prr(m)
sat_pos_vel[i] = sat_pos_vel_i
R[i,:,:] = R_i
z[i, :] = z_i
return self.filter.predict_and_update_batch(t, kind, z, R, sat_pos_vel)
if __name__ == "__main__":
GNSSKalman.generate_code()

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selfdrive/locationd/kalman/models/live_kf.py 100755
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#!/usr/bin/env python3
import numpy as np
import sympy as sp
from laika.constants import EARTH_GM
from selfdrive.locationd.kalman.helpers import KalmanError, ObservationKind
from selfdrive.locationd.kalman.helpers.ekf_sym import EKF_sym, gen_code
from selfdrive.locationd.kalman.helpers.sympy_helpers import (euler_rotate,
quat_matrix_r,
quat_rotate)
from selfdrive.swaglog import cloudlog
class States():
ECEF_POS = slice(0, 3) # x, y and z in ECEF in meters
ECEF_ORIENTATION = slice(3, 7) # quat for pose of phone in ecef
ECEF_VELOCITY = slice(7, 10) # ecef velocity in m/s
ANGULAR_VELOCITY = slice(10, 13) # roll, pitch and yaw rates in device frame in radians/s
GYRO_BIAS = slice(13, 16) # roll, pitch and yaw biases
ODO_SCALE = slice(16, 17) # odometer scale
ACCELERATION = slice(17, 20) # Acceleration in device frame in m/s**2
IMU_OFFSET = slice(20, 23) # imu offset angles in radians
ECEF_POS_ERR = slice(0, 3)
ECEF_ORIENTATION_ERR = slice(3, 6)
ECEF_VELOCITY_ERR = slice(6, 9)
ANGULAR_VELOCITY_ERR = slice(9, 12)
GYRO_BIAS_ERR = slice(12, 15)
ODO_SCALE_ERR = slice(15, 16)
ACCELERATION_ERR = slice(16, 19)
IMU_OFFSET_ERR = slice(19, 22)
class LiveKalman():
name = 'live'
initial_x = np.array([-2.7e6, 4.2e6, 3.8e6,
1, 0, 0, 0,
0, 0, 0,
0, 0, 0,
0, 0, 0,
1,
0, 0, 0,
0, 0, 0])
# state covariance
initial_P_diag = np.array([10000**2, 10000**2, 10000**2,
10**2, 10**2, 10**2,
10**2, 10**2, 10**2,
1**2, 1**2, 1**2,
0.05**2, 0.05**2, 0.05**2,
0.02**2,
1**2, 1**2, 1**2,
(0.01)**2, (0.01)**2, (0.01)**2])
# process noise
Q = np.diag([0.03**2, 0.03**2, 0.03**2,
0.0**2, 0.0**2, 0.0**2,
0.0**2, 0.0**2, 0.0**2,
0.1**2, 0.1**2, 0.1**2,
(0.005/100)**2, (0.005/100)**2, (0.005/100)**2,
(0.02/100)**2,
3**2, 3**2, 3**2,
(0.05/60)**2, (0.05/60)**2, (0.05/60)**2])
@staticmethod
def generate_code():
name = LiveKalman.name
dim_state = LiveKalman.initial_x.shape[0]
dim_state_err = LiveKalman.initial_P_diag.shape[0]
state_sym = sp.MatrixSymbol('state', dim_state, 1)
state = sp.Matrix(state_sym)
x,y,z = state[States.ECEF_POS,:]
q = state[States.ECEF_ORIENTATION,:]
v = state[States.ECEF_VELOCITY,:]
vx, vy, vz = v
omega = state[States.ANGULAR_VELOCITY,:]
vroll, vpitch, vyaw = omega
roll_bias, pitch_bias, yaw_bias = state[States.GYRO_BIAS,:]
odo_scale = state[16,:]
acceleration = state[States.ACCELERATION,:]
imu_angles= state[States.IMU_OFFSET,:]
dt = sp.Symbol('dt')
# calibration and attitude rotation matrices
quat_rot = quat_rotate(*q)
# Got the quat predict equations from here
# A New Quaternion-Based Kalman Filter for
# Real-Time Attitude Estimation Using the Two-Step
# Geometrically-Intuitive Correction Algorithm
A = 0.5*sp.Matrix([[0, -vroll, -vpitch, -vyaw],
[vroll, 0, vyaw, -vpitch],
[vpitch, -vyaw, 0, vroll],
[vyaw, vpitch, -vroll, 0]])
q_dot = A * q
# Time derivative of the state as a function of state
state_dot = sp.Matrix(np.zeros((dim_state, 1)))
state_dot[States.ECEF_POS,:] = v
state_dot[States.ECEF_ORIENTATION,:] = q_dot
state_dot[States.ECEF_VELOCITY,0] = quat_rot * acceleration
# Basic descretization, 1st order intergrator
# Can be pretty bad if dt is big
f_sym = state + dt*state_dot
state_err_sym = sp.MatrixSymbol('state_err',dim_state_err,1)
state_err = sp.Matrix(state_err_sym)
quat_err = state_err[States.ECEF_ORIENTATION_ERR,:]
v_err = state_err[States.ECEF_VELOCITY_ERR,:]
omega_err = state_err[States.ANGULAR_VELOCITY_ERR,:]
acceleration_err = state_err[States.ACCELERATION_ERR,:]
# Time derivative of the state error as a function of state error and state
quat_err_matrix = euler_rotate(quat_err[0], quat_err[1], quat_err[2])
q_err_dot = quat_err_matrix * quat_rot * (omega + omega_err)
state_err_dot = sp.Matrix(np.zeros((dim_state_err, 1)))
state_err_dot[States.ECEF_POS_ERR,:] = v_err
state_err_dot[States.ECEF_ORIENTATION_ERR,:] = q_err_dot
state_err_dot[States.ECEF_VELOCITY_ERR,:] = quat_err_matrix * quat_rot * (acceleration + acceleration_err)
f_err_sym = state_err + dt*state_err_dot
# Observation matrix modifier
H_mod_sym = sp.Matrix(np.zeros((dim_state, dim_state_err)))
H_mod_sym[0:3, 0:3] = np.eye(3)
H_mod_sym[3:7,3:6] = 0.5*quat_matrix_r(state[3:7])[:,1:]
H_mod_sym[7:, 6:] = np.eye(dim_state-7)
# these error functions are defined so that say there
# is a nominal x and true x:
# true x = err_function(nominal x, delta x)
# delta x = inv_err_function(nominal x, true x)
nom_x = sp.MatrixSymbol('nom_x',dim_state,1)
true_x = sp.MatrixSymbol('true_x',dim_state,1)
delta_x = sp.MatrixSymbol('delta_x',dim_state_err,1)
err_function_sym = sp.Matrix(np.zeros((dim_state,1)))
delta_quat = sp.Matrix(np.ones((4)))
delta_quat[1:,:] = sp.Matrix(0.5*delta_x[3:6,:])
err_function_sym[0:3,:] = sp.Matrix(nom_x[0:3,:] + delta_x[0:3,:])
err_function_sym[3:7,0] = quat_matrix_r(nom_x[3:7,0])*delta_quat
err_function_sym[7:,:] = sp.Matrix(nom_x[7:,:] + delta_x[6:,:])
inv_err_function_sym = sp.Matrix(np.zeros((dim_state_err,1)))
inv_err_function_sym[0:3,0] = sp.Matrix(-nom_x[0:3,0] + true_x[0:3,0])
delta_quat = quat_matrix_r(nom_x[3:7,0]).T*true_x[3:7,0]
inv_err_function_sym[3:6,0] = sp.Matrix(2*delta_quat[1:])
inv_err_function_sym[6:,0] = sp.Matrix(-nom_x[7:,0] + true_x[7:,0])
eskf_params = [[err_function_sym, nom_x, delta_x],
[inv_err_function_sym, nom_x, true_x],
H_mod_sym, f_err_sym, state_err_sym]
#
# Observation functions
#
imu_rot = euler_rotate(*imu_angles)
h_gyro_sym = imu_rot*sp.Matrix([vroll + roll_bias,
vpitch + pitch_bias,
vyaw + yaw_bias])
pos = sp.Matrix([x, y, z])
gravity = quat_rot.T * ((EARTH_GM/((x**2 + y**2 + z**2)**(3.0/2.0)))*pos)
h_acc_sym = imu_rot*(gravity + acceleration)
h_phone_rot_sym = sp.Matrix([vroll,
vpitch,
vyaw])
speed = vx**2 + vy**2 + vz**2
h_speed_sym = sp.Matrix([sp.sqrt(speed)*odo_scale])
h_pos_sym = sp.Matrix([x, y, z])
h_imu_frame_sym = sp.Matrix(imu_angles)
h_relative_motion = sp.Matrix(quat_rot.T * v)
obs_eqs = [[h_speed_sym, ObservationKind.ODOMETRIC_SPEED, None],
[h_gyro_sym, ObservationKind.PHONE_GYRO, None],
[h_phone_rot_sym, ObservationKind.NO_ROT, None],
[h_acc_sym, ObservationKind.PHONE_ACCEL, None],
[h_pos_sym, ObservationKind.ECEF_POS, None],
[h_relative_motion, ObservationKind.CAMERA_ODO_TRANSLATION, None],
[h_phone_rot_sym, ObservationKind.CAMERA_ODO_ROTATION, None],
[h_imu_frame_sym, ObservationKind.IMU_FRAME, None]]
gen_code(name, f_sym, dt, state_sym, obs_eqs, dim_state, dim_state_err, eskf_params)
def __init__(self):
self.dim_state = self.initial_x.shape[0]
self.dim_state_err = self.initial_P_diag.shape[0]
self.obs_noise = {ObservationKind.ODOMETRIC_SPEED: np.atleast_2d(0.2**2),
ObservationKind.PHONE_GYRO: np.diag([0.025**2, 0.025**2, 0.025**2]),
ObservationKind.PHONE_ACCEL: np.diag([.5**2, .5**2, .5*2]),
ObservationKind.CAMERA_ODO_ROTATION: np.diag([0.05**2, 0.05**2, 0.05**2]),
ObservationKind.IMU_FRAME: np.diag([0.05**2, 0.05**2, 0.05**2]),
ObservationKind.NO_ROT: np.diag([0.00025**2, 0.00025**2, 0.00025**2]),
ObservationKind.ECEF_POS: np.diag([5**2, 5**2, 5**2])}
# init filter
self.filter = EKF_sym(self.name, self.Q, self.initial_x, np.diag(self.initial_P_diag), self.dim_state, self.dim_state_err)
@property
def x(self):
return self.filter.state()
@property
def t(self):
return self.filter.filter_time
@property
def P(self):
return self.filter.covs()
def predict(self, t):
return self.filter.predict(t)
def rts_smooth(self, estimates):
return self.filter.rts_smooth(estimates, norm_quats=True)
def init_state(self, state, covs_diag=None, covs=None, filter_time=None):
if covs_diag is not None:
P = np.diag(covs_diag)
elif covs is not None:
P = covs
else:
P = self.filter.covs()
self.filter.init_state(state, P, filter_time)
def predict_and_observe(self, t, kind, data):
if len(data) > 0:
data = np.atleast_2d(data)
if kind == ObservationKind.CAMERA_ODO_TRANSLATION:
r = self.predict_and_update_odo_trans(data, t, kind)
elif kind == ObservationKind.CAMERA_ODO_ROTATION:
r = self.predict_and_update_odo_rot(data, t, kind)
elif kind == ObservationKind.ODOMETRIC_SPEED:
r = self.predict_and_update_odo_speed(data, t, kind)
else:
r = self.filter.predict_and_update_batch(t, kind, data, self.get_R(kind, len(data)))
# Normalize quats
quat_norm = np.linalg.norm(self.filter.x[3:7, 0])
# Should not continue if the quats behave this weirdly
if not (0.1 < quat_norm < 10):
cloudlog.error("Kalman filter quaternions unstable")
raise KalmanError
self.filter.x[States.ECEF_ORIENTATION, 0] = self.filter.x[States.ECEF_ORIENTATION, 0] / quat_norm
return r
def get_R(self, kind, n):
obs_noise = self.obs_noise[kind]
dim = obs_noise.shape[0]
R = np.zeros((n, dim, dim))
for i in range(n):
R[i, :, :] = obs_noise
return R
def predict_and_update_odo_speed(self, speed, t, kind):
z = np.array(speed)
R = np.zeros((len(speed), 1, 1))
for i, _ in enumerate(z):
R[i, :, :] = np.diag([0.2**2])
return self.filter.predict_and_update_batch(t, kind, z, R)
def predict_and_update_odo_trans(self, trans, t, kind):
z = trans[:, :3]
R = np.zeros((len(trans), 3, 3))
for i, _ in enumerate(z):
R[i, :, :] = np.diag(trans[i, 3:]**2)
return self.filter.predict_and_update_batch(t, kind, z, R)
def predict_and_update_odo_rot(self, rot, t, kind):
z = rot[:, :3]
R = np.zeros((len(rot), 3, 3))
for i, _ in enumerate(z):
R[i, :, :] = np.diag(rot[i, 3:]**2)
return self.filter.predict_and_update_batch(t, kind, z, R)
if __name__ == "__main__":
LiveKalman.generate_code()

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selfdrive/locationd/kalman/models/loc_kf.py 100755
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#!/usr/bin/env python3
import os
import numpy as np
import sympy as sp
from laika.constants import EARTH_GM
from laika.raw_gnss import GNSSMeasurement
from selfdrive.locationd.kalman.helpers import ObservationKind
from selfdrive.locationd.kalman.helpers.ekf_sym import EKF_sym, gen_code
from selfdrive.locationd.kalman.helpers.lst_sq_computer import LstSqComputer
from selfdrive.locationd.kalman.helpers.sympy_helpers import (euler_rotate,
quat_matrix_r,
quat_rotate)
def parse_prr(m):
sat_pos_vel_i = np.concatenate((m[GNSSMeasurement.SAT_POS],
m[GNSSMeasurement.SAT_VEL]))
R_i = np.atleast_2d(m[GNSSMeasurement.PRR_STD]**2)
z_i = m[GNSSMeasurement.PRR]
return z_i, R_i, sat_pos_vel_i
def parse_pr(m):
pseudorange = m[GNSSMeasurement.PR]
pseudorange_stdev = m[GNSSMeasurement.PR_STD]
sat_pos_freq_i = np.concatenate((m[GNSSMeasurement.SAT_POS],
np.array([m[GNSSMeasurement.GLONASS_FREQ]])))
z_i = np.atleast_1d(pseudorange)
R_i = np.atleast_2d(pseudorange_stdev**2)
return z_i, R_i, sat_pos_freq_i
class States():
ECEF_POS = slice(0,3) # x, y and z in ECEF in meters
ECEF_ORIENTATION = slice(3,7) # quat for pose of phone in ecef
ECEF_VELOCITY = slice(7,10) # ecef velocity in m/s
ANGULAR_VELOCITY = slice(10, 13) # roll, pitch and yaw rates in device frame in radians/s
CLOCK_BIAS = slice(13, 14) # clock bias in light-meters,
CLOCK_DRIFT = slice(14, 15) # clock drift in light-meters/s,
GYRO_BIAS = slice(15, 18) # roll, pitch and yaw biases
ODO_SCALE = slice(18, 19) # odometer scale
ACCELERATION = slice(19, 22) # Acceleration in device frame in m/s**2
FOCAL_SCALE = slice(22, 23) # focal length scale
IMU_OFFSET = slice(23,26) # imu offset angles in radians
GLONASS_BIAS = slice(26,27) # GLONASS bias in m expressed as bias + freq_num*freq_slope
GLONASS_FREQ_SLOPE = slice(27, 28) # GLONASS bias in m expressed as bias + freq_num*freq_slope
CLOCK_ACCELERATION = slice(28, 29) # clock acceleration in light-meters/s**2,
class LocKalman():
name = "loc"
x_initial = np.array([-2.7e6, 4.2e6, 3.8e6,
1, 0, 0, 0,
0, 0, 0,
0, 0, 0,
0, 0,
0, 0, 0,
1,
0, 0, 0,
1,
0, 0, 0,
0, 0,
0])
# state covariance
P_initial = np.diag([10000**2, 10000**2, 10000**2,
10**2, 10**2, 10**2,
10**2, 10**2, 10**2,
1**2, 1**2, 1**2,
(200000)**2, (100)**2,
0.05**2, 0.05**2, 0.05**2,
0.02**2,
1**2, 1**2, 1**2,
0.01**2,
(0.01)**2, (0.01)**2, (0.01)**2,
10**2, 1**2,
0.05**2])
# process noise
Q = np.diag([0.03**2, 0.03**2, 0.03**2,
0.0**2, 0.0**2, 0.0**2,
0.0**2, 0.0**2, 0.0**2,
0.1**2, 0.1**2, 0.1**2,
(.1)**2, (0.0)**2,
(0.005/100)**2, (0.005/100)**2, (0.005/100)**2,
(0.02/100)**2,
3**2, 3**2, 3**2,
0.001**2,
(0.05/60)**2, (0.05/60)**2, (0.05/60)**2,
(.1)**2, (.01)**2,
0.005**2])
maha_test_kinds = [ObservationKind.ORB_FEATURES] #, ObservationKind.PSEUDORANGE, ObservationKind.PSEUDORANGE_RATE]
dim_augment = 7
dim_augment_err = 6
@staticmethod
def generate_code(N=4):
dim_augment = LocKalman.dim_augment
dim_augment_err = LocKalman.dim_augment_err
dim_main = LocKalman.x_initial.shape[0]
dim_main_err = LocKalman.P_initial.shape[0]
dim_state = dim_main + dim_augment * N
dim_state_err = dim_main_err + dim_augment_err * N
maha_test_kinds = LocKalman.maha_test_kinds
name = f"{LocKalman.name}_{N}"
# make functions and jacobians with sympy
# state variables
state_sym = sp.MatrixSymbol('state', dim_state, 1)
state = sp.Matrix(state_sym)
x,y,z = state[0:3,:]
q = state[3:7,:]
v = state[7:10,:]
vx, vy, vz = v
omega = state[10:13,:]
vroll, vpitch, vyaw = omega
cb, cd = state[13:15,:]
roll_bias, pitch_bias, yaw_bias = state[15:18,:]
odo_scale = state[18,:]
acceleration = state[19:22,:]
focal_scale = state[22,:]
imu_angles= state[23:26,:]
glonass_bias, glonass_freq_slope = state[26:28,:]
ca = state[28,0]
dt = sp.Symbol('dt')
# calibration and attitude rotation matrices
quat_rot = quat_rotate(*q)
# Got the quat predict equations from here
# A New Quaternion-Based Kalman Filter for
# Real-Time Attitude Estimation Using the Two-Step
# Geometrically-Intuitive Correction Algorithm
A = 0.5*sp.Matrix([[0, -vroll, -vpitch, -vyaw],
[vroll, 0, vyaw, -vpitch],
[vpitch, -vyaw, 0, vroll],
[vyaw, vpitch, -vroll, 0]])
q_dot = A * q
# Time derivative of the state as a function of state
state_dot = sp.Matrix(np.zeros((dim_state, 1)))
state_dot[:3,:] = v
state_dot[3:7,:] = q_dot
state_dot[7:10,0] = quat_rot * acceleration
state_dot[13,0] = cd
state_dot[14,0] = ca
# Basic descretization, 1st order intergrator
# Can be pretty bad if dt is big
f_sym = state + dt*state_dot
state_err_sym = sp.MatrixSymbol('state_err',dim_state_err,1)
state_err = sp.Matrix(state_err_sym)
quat_err = state_err[3:6,:]
v_err = state_err[6:9,:]
omega_err = state_err[9:12,:]
cd_err = state_err[13,:]
acceleration_err = state_err[18:21,:]
ca_err = state_err[27,:]
# Time derivative of the state error as a function of state error and state
quat_err_matrix = euler_rotate(quat_err[0], quat_err[1], quat_err[2])
q_err_dot = quat_err_matrix * quat_rot * (omega + omega_err)
state_err_dot = sp.Matrix(np.zeros((dim_state_err, 1)))
state_err_dot[:3,:] = v_err
state_err_dot[3:6,:] = q_err_dot
state_err_dot[6:9,:] = quat_err_matrix * quat_rot * (acceleration + acceleration_err)
state_err_dot[12,:] = cd_err
state_err_dot[13,:] = ca_err
f_err_sym = state_err + dt*state_err_dot
# convenient indexing
# q idxs are for quats and p idxs are for other
q_idxs = [[3, dim_augment]] + [[dim_main + n*dim_augment + 3, dim_main + (n+1)*dim_augment] for n in range(N)]
q_err_idxs = [[3, dim_augment_err]] + [[dim_main_err + n*dim_augment_err + 3, dim_main_err + (n+1)*dim_augment_err] for n in range(N)]
p_idxs = [[0, 3]] + [[dim_augment, dim_main]] + [[dim_main + n*dim_augment , dim_main + n*dim_augment + 3] for n in range(N)]
p_err_idxs = [[0, 3]] + [[dim_augment_err, dim_main_err]] + [[dim_main_err + n*dim_augment_err, dim_main_err + n*dim_augment_err + 3] for n in range(N)]
# Observation matrix modifier
H_mod_sym = sp.Matrix(np.zeros((dim_state, dim_state_err)))
for p_idx, p_err_idx in zip(p_idxs, p_err_idxs):
H_mod_sym[p_idx[0]:p_idx[1],p_err_idx[0]:p_err_idx[1]] = np.eye(p_idx[1]-p_idx[0])
for q_idx, q_err_idx in zip(q_idxs, q_err_idxs):
H_mod_sym[q_idx[0]:q_idx[1],q_err_idx[0]:q_err_idx[1]] = 0.5*quat_matrix_r(state[q_idx[0]:q_idx[1]])[:,1:]
# these error functions are defined so that say there
# is a nominal x and true x:
# true x = err_function(nominal x, delta x)
# delta x = inv_err_function(nominal x, true x)
nom_x = sp.MatrixSymbol('nom_x',dim_state,1)
true_x = sp.MatrixSymbol('true_x',dim_state,1)
delta_x = sp.MatrixSymbol('delta_x',dim_state_err,1)
err_function_sym = sp.Matrix(np.zeros((dim_state,1)))
for q_idx, q_err_idx in zip(q_idxs, q_err_idxs):
delta_quat = sp.Matrix(np.ones((4)))
delta_quat[1:,:] = sp.Matrix(0.5*delta_x[q_err_idx[0]: q_err_idx[1],:])
err_function_sym[q_idx[0]:q_idx[1],0] = quat_matrix_r(nom_x[q_idx[0]:q_idx[1],0])*delta_quat
for p_idx, p_err_idx in zip(p_idxs, p_err_idxs):
err_function_sym[p_idx[0]:p_idx[1],:] = sp.Matrix(nom_x[p_idx[0]:p_idx[1],:] + delta_x[p_err_idx[0]:p_err_idx[1],:])
inv_err_function_sym = sp.Matrix(np.zeros((dim_state_err,1)))
for p_idx, p_err_idx in zip(p_idxs, p_err_idxs):
inv_err_function_sym[p_err_idx[0]:p_err_idx[1],0] = sp.Matrix(-nom_x[p_idx[0]:p_idx[1],0] + true_x[p_idx[0]:p_idx[1],0])
for q_idx, q_err_idx in zip(q_idxs, q_err_idxs):
delta_quat = quat_matrix_r(nom_x[q_idx[0]:q_idx[1],0]).T*true_x[q_idx[0]:q_idx[1],0]
inv_err_function_sym[q_err_idx[0]:q_err_idx[1],0] = sp.Matrix(2*delta_quat[1:])
eskf_params = [[err_function_sym, nom_x, delta_x],
[inv_err_function_sym, nom_x, true_x],
H_mod_sym, f_err_sym, state_err_sym]
#
# Observation functions
#
# extra args
sat_pos_freq_sym = sp.MatrixSymbol('sat_pos', 4, 1)
sat_pos_vel_sym = sp.MatrixSymbol('sat_pos_vel', 6, 1)
sat_los_sym = sp.MatrixSymbol('sat_los', 3, 1)
orb_epos_sym = sp.MatrixSymbol('orb_epos_sym', 3, 1)
# expand extra args
sat_x, sat_y, sat_z, glonass_freq = sat_pos_freq_sym
sat_vx, sat_vy, sat_vz = sat_pos_vel_sym[3:]
los_x, los_y, los_z = sat_los_sym
orb_x, orb_y, orb_z = orb_epos_sym
h_pseudorange_sym = sp.Matrix([sp.sqrt(
(x - sat_x)**2 +
(y - sat_y)**2 +
(z - sat_z)**2) +
cb])
h_pseudorange_glonass_sym = sp.Matrix([sp.sqrt(
(x - sat_x)**2 +
(y - sat_y)**2 +
(z - sat_z)**2) +
cb + glonass_bias + glonass_freq_slope*glonass_freq])
los_vector = (sp.Matrix(sat_pos_vel_sym[0:3]) - sp.Matrix([x, y, z]))
los_vector = los_vector / sp.sqrt(los_vector[0]**2 + los_vector[1]**2 + los_vector[2]**2)
h_pseudorange_rate_sym = sp.Matrix([los_vector[0]*(sat_vx - vx) +
los_vector[1]*(sat_vy - vy) +
los_vector[2]*(sat_vz - vz) +
cd])
imu_rot = euler_rotate(*imu_angles)
h_gyro_sym = imu_rot*sp.Matrix([vroll + roll_bias,
vpitch + pitch_bias,
vyaw + yaw_bias])
pos = sp.Matrix([x, y, z])
gravity = quat_rot.T * ((EARTH_GM/((x**2 + y**2 + z**2)**(3.0/2.0)))*pos)
h_acc_sym = imu_rot*(gravity + acceleration)
h_phone_rot_sym = sp.Matrix([vroll,
vpitch,
vyaw])
speed = vx**2 + vy**2 + vz**2
h_speed_sym = sp.Matrix([sp.sqrt(speed)*odo_scale])
# orb stuff
orb_pos_sym = sp.Matrix([orb_x - x, orb_y - y, orb_z - z])
orb_pos_rot_sym = quat_rot.T * orb_pos_sym
s = orb_pos_rot_sym[0]
h_orb_point_sym = sp.Matrix([(1/s)*(orb_pos_rot_sym[1]),
(1/s)*(orb_pos_rot_sym[2])])
h_pos_sym = sp.Matrix([x, y, z])
h_imu_frame_sym = sp.Matrix(imu_angles)
h_relative_motion = sp.Matrix(quat_rot.T * v)
obs_eqs = [[h_speed_sym, ObservationKind.ODOMETRIC_SPEED, None],
[h_gyro_sym, ObservationKind.PHONE_GYRO, None],
[h_phone_rot_sym, ObservationKind.NO_ROT, None],
[h_acc_sym, ObservationKind.PHONE_ACCEL, None],
[h_pseudorange_sym, ObservationKind.PSEUDORANGE_GPS, sat_pos_freq_sym],
[h_pseudorange_glonass_sym, ObservationKind.PSEUDORANGE_GLONASS, sat_pos_freq_sym],
[h_pseudorange_rate_sym, ObservationKind.PSEUDORANGE_RATE_GPS, sat_pos_vel_sym],
[h_pseudorange_rate_sym, ObservationKind.PSEUDORANGE_RATE_GLONASS, sat_pos_vel_sym],
[h_pos_sym, ObservationKind.ECEF_POS, None],
[h_relative_motion, ObservationKind.CAMERA_ODO_TRANSLATION, None],
[h_phone_rot_sym, ObservationKind.CAMERA_ODO_ROTATION, None],
[h_imu_frame_sym, ObservationKind.IMU_FRAME, None],
[h_orb_point_sym, ObservationKind.ORB_POINT, orb_epos_sym]]
# MSCKF configuration
if N > 0:
focal_scale =1
# Add observation functions for orb feature tracks
track_epos_sym = sp.MatrixSymbol('track_epos_sym', 3, 1)
track_x, track_y, track_z = track_epos_sym
h_track_sym = sp.Matrix(np.zeros(((1 + N)*2, 1)))
track_pos_sym = sp.Matrix([track_x - x, track_y - y, track_z - z])
track_pos_rot_sym = quat_rot.T * track_pos_sym
h_track_sym[-2:,:] = sp.Matrix([focal_scale*(track_pos_rot_sym[1]/track_pos_rot_sym[0]),
focal_scale*(track_pos_rot_sym[2]/track_pos_rot_sym[0])])
h_msckf_test_sym = sp.Matrix(np.zeros(((1 + N)*3, 1)))
h_msckf_test_sym[-3:,:] = sp.Matrix([track_x - x,track_y - y , track_z - z])
for n in range(N):
idx = dim_main + n*dim_augment
err_idx = dim_main_err + n*dim_augment_err
x, y, z = state[idx:idx+3]
q = state[idx+3:idx+7]
quat_rot = quat_rotate(*q)
track_pos_sym = sp.Matrix([track_x - x, track_y - y, track_z - z])
track_pos_rot_sym = quat_rot.T * track_pos_sym
h_track_sym[n*2:n*2+2,:] = sp.Matrix([focal_scale*(track_pos_rot_sym[1]/track_pos_rot_sym[0]),
focal_scale*(track_pos_rot_sym[2]/track_pos_rot_sym[0])])
h_msckf_test_sym[n*3:n*3+3,:] = sp.Matrix([track_x - x, track_y - y, track_z - z])
obs_eqs.append([h_msckf_test_sym, ObservationKind.MSCKF_TEST, track_epos_sym])
obs_eqs.append([h_track_sym, ObservationKind.ORB_FEATURES, track_epos_sym])
obs_eqs.append([h_track_sym, ObservationKind.FEATURE_TRACK_TEST, track_epos_sym])
msckf_params = [dim_main, dim_augment, dim_main_err, dim_augment_err, N, [ObservationKind.MSCKF_TEST, ObservationKind.ORB_FEATURES]]
else:
msckf_params = None
gen_code(name, f_sym, dt, state_sym, obs_eqs, dim_state, dim_state_err, eskf_params, msckf_params, maha_test_kinds)
def __init__(self, N=4, max_tracks=3000):
name = f"{self.name}_{N}"
self.obs_noise = {ObservationKind.ODOMETRIC_SPEED: np.atleast_2d(0.2**2),
ObservationKind.PHONE_GYRO: np.diag([0.025**2, 0.025**2, 0.025**2]),
ObservationKind.PHONE_ACCEL: np.diag([.5**2, .5**2, .5*2]),
ObservationKind.CAMERA_ODO_ROTATION: np.diag([0.05**2, 0.05**2, 0.05**2]),
ObservationKind.IMU_FRAME: np.diag([0.05**2, 0.05**2, 0.05**2]),
ObservationKind.NO_ROT: np.diag([0.00025**2, 0.00025**2, 0.00025**2]),
ObservationKind.ECEF_POS: np.diag([5**2, 5**2, 5**2])}
# MSCKF stuff
self.N = N
self.dim_main = LocKalman.x_initial.shape[0]
self.dim_main_err = LocKalman.P_initial.shape[0]
self.dim_state = self.dim_main + self.dim_augment*self.N
self.dim_state_err = self.dim_main_err + self.dim_augment_err*self.N
if self.N > 0:
x_initial, P_initial, Q = self.pad_augmented(self.x_initial, self.P_initial, self.Q)
self.computer = LstSqComputer(N)
self.max_tracks = max_tracks
# init filter
self.filter = EKF_sym(name, Q, x_initial, P_initial, self.dim_main, self.dim_main_err,
N, self.dim_augment, self.dim_augment_err, self.maha_test_kinds)
@property
def x(self):
return self.filter.state()
@property
def t(self):
return self.filter.filter_time
@property
def P(self):
return self.filter.covs()
def predict(self, t):
return self.filter.predict(t)
def rts_smooth(self, estimates):
return self.filter.rts_smooth(estimates, norm_quats=True)
def pad_augmented(self, x, P, Q=None):
if x.shape[0] == self.dim_main and self.N > 0:
x = np.pad(x, (0, self.N*self.dim_augment), mode='constant')
x[self.dim_main+3::7] = 1
if P.shape[0] == self.dim_main_err and self.N > 0:
P = np.pad(P, [(0, self.N*self.dim_augment_err), (0, self.N*self.dim_augment_err)], mode='constant')
P[self.dim_main_err:, self.dim_main_err:] = 10e20*np.eye(self.dim_augment_err *self.N)
if Q is None:
return x, P
else:
Q = np.pad(Q, [(0, self.N*self.dim_augment_err), (0, self.N*self.dim_augment_err)], mode='constant')
return x, P, Q
def init_state(self, state, covs_diag=None, covs=None, filter_time=None):
if covs_diag is not None:
P = np.diag(covs_diag)
elif covs is not None:
P = covs
else:
P = self.filter.covs()
state, P = self.pad_augmented(state, P)
self.filter.init_state(state, P, filter_time)
def predict_and_observe(self, t, kind, data):
if len(data) > 0:
data = np.atleast_2d(data)
if kind == ObservationKind.CAMERA_ODO_TRANSLATION:
r = self.predict_and_update_odo_trans(data, t, kind)
elif kind == ObservationKind.CAMERA_ODO_ROTATION:
r = self.predict_and_update_odo_rot(data, t, kind)
elif kind == ObservationKind.PSEUDORANGE_GPS or kind == ObservationKind.PSEUDORANGE_GLONASS:
r = self.predict_and_update_pseudorange(data, t, kind)
elif kind == ObservationKind.PSEUDORANGE_RATE_GPS or kind == ObservationKind.PSEUDORANGE_RATE_GLONASS:
r = self.predict_and_update_pseudorange_rate(data, t, kind)
elif kind == ObservationKind.ORB_POINT:
r = self.predict_and_update_orb(data, t, kind)
elif kind == ObservationKind.ORB_FEATURES:
r = self.predict_and_update_orb_features(data, t, kind)
elif kind == ObservationKind.MSCKF_TEST:
r = self.predict_and_update_msckf_test(data, t, kind)
elif kind == ObservationKind.FEATURE_TRACK_TEST:
r = self.predict_and_update_feature_track_test(data, t, kind)
elif kind == ObservationKind.ODOMETRIC_SPEED:
r = self.predict_and_update_odo_speed(data, t, kind)
else:
r = self.filter.predict_and_update_batch(t, kind, data, self.get_R(kind, len(data)))
# Normalize quats
quat_norm = np.linalg.norm(self.filter.x[3:7,0])
# Should not continue if the quats behave this weirdly
if not 0.1 < quat_norm < 10:
raise RuntimeError("Sir! The filter's gone all wobbly!")
self.filter.x[3:7,0] = self.filter.x[3:7,0]/quat_norm
for i in range(self.N):
d1 = self.dim_main
d3 = self.dim_augment
self.filter.x[d1+d3*i+3:d1+d3*i+7] /= np.linalg.norm(self.filter.x[d1+i*d3 + 3:d1+i*d3 + 7,0])
return r
def get_R(self, kind, n):
obs_noise = self.obs_noise[kind]
dim = obs_noise.shape[0]
R = np.zeros((n, dim, dim))
for i in range(n):
R[i,:,:] = obs_noise
return R
def predict_and_update_pseudorange(self, meas, t, kind):
R = np.zeros((len(meas), 1, 1))
sat_pos_freq = np.zeros((len(meas), 4))
z = np.zeros((len(meas), 1))
for i, m in enumerate(meas):
z_i, R_i, sat_pos_freq_i = parse_pr(m)
sat_pos_freq[i,:] = sat_pos_freq_i
z[i,:] = z_i
R[i,:,:] = R_i
return self.filter.predict_and_update_batch(t, kind, z, R, sat_pos_freq)
def predict_and_update_pseudorange_rate(self, meas, t, kind):
R = np.zeros((len(meas), 1, 1))
z = np.zeros((len(meas), 1))
sat_pos_vel = np.zeros((len(meas), 6))
for i, m in enumerate(meas):
z_i, R_i, sat_pos_vel_i = parse_prr(m)
sat_pos_vel[i] = sat_pos_vel_i
R[i,:,:] = R_i
z[i, :] = z_i
return self.filter.predict_and_update_batch(t, kind, z, R, sat_pos_vel)
def predict_and_update_orb(self, orb, t, kind):
true_pos = orb[:,2:]
z = orb[:,:2]
R = np.zeros((len(orb), 2, 2))
for i, _ in enumerate(z):
R[i,:,:] = np.diag([10**2, 10**2])
return self.filter.predict_and_update_batch(t, kind, z, R, true_pos)
def predict_and_update_odo_speed(self, speed, t, kind):
z = np.array(speed)
R = np.zeros((len(speed), 1, 1))
for i, _ in enumerate(z):
R[i,:,:] = np.diag([0.2**2])
return self.filter.predict_and_update_batch(t, kind, z, R)
def predict_and_update_odo_trans(self, trans, t, kind):
z = trans[:,:3]
R = np.zeros((len(trans), 3, 3))
for i, _ in enumerate(z):
R[i,:,:] = np.diag(trans[i,3:]**2)
return self.filter.predict_and_update_batch(t, kind, z, R)
def predict_and_update_odo_rot(self, rot, t, kind):
z = rot[:,:3]
R = np.zeros((len(rot), 3, 3))
for i, _ in enumerate(z):
R[i,:,:] = np.diag(rot[i,3:]**2)
return self.filter.predict_and_update_batch(t, kind, z, R)
def predict_and_update_orb_features(self, tracks, t, kind):
k = 2*(self.N+1)
R = np.zeros((len(tracks), k, k))
z = np.zeros((len(tracks), k))
ecef_pos = np.zeros((len(tracks), 3))
ecef_pos[:] = np.nan
poses = self.x[self.dim_main:].reshape((-1,7))
times = tracks.reshape((len(tracks),self.N+1, 4))[:,:,0]
good_counter = 0
if times.any() and np.allclose(times[0,:-1], self.filter.augment_times, rtol=1e-6):
for i, track in enumerate(tracks):
img_positions = track.reshape((self.N+1, 4))[:,2:]
# TODO not perfect as last pose not used
#img_positions = unroll_shutter(img_positions, poses, self.filter.state()[7:10], self.filter.state()[10:13], ecef_pos[i])
ecef_pos[i] = self.computer.compute_pos(poses, img_positions[:-1])
z[i] = img_positions.flatten()
R[i,:,:] = np.diag([0.005**2]*(k))
if np.isfinite(ecef_pos[i][0]):
good_counter += 1
if good_counter > self.max_tracks:
break
good_idxs = np.all(np.isfinite(ecef_pos),axis=1)
# have to do some weird stuff here to keep
# to have the observations input from mesh3d
# consistent with the outputs of the filter
# Probably should be replaced, not sure how.
ret = self.filter.predict_and_update_batch(t, kind, z[good_idxs], R[good_idxs], ecef_pos[good_idxs], augment=True)
if ret is None:
return
y_full = np.zeros((z.shape[0], z.shape[1] - 3))
#print sum(good_idxs), len(tracks)
if sum(good_idxs) > 0:
y_full[good_idxs] = np.array(ret[6])
ret = ret[:6] + (y_full, z, ecef_pos)
return ret
def predict_and_update_msckf_test(self, test_data, t, kind):
assert self.N > 0
z = test_data
R = np.zeros((len(test_data), len(z[0]), len(z[0])))
ecef_pos = [self.x[:3]]
for i, _ in enumerate(z):
R[i,:,:] = np.diag([0.1**2]*len(z[0]))
ret = self.filter.predict_and_update_batch(t, kind, z, R, ecef_pos)
self.filter.augment()
return ret
def maha_test_pseudorange(self, x, P, meas, kind, maha_thresh=.3):
bools = []
for i, m in enumerate(meas):
z, R, sat_pos_freq = parse_pr(m)
bools.append(self.filter.maha_test(x, P, kind, z, R, extra_args=sat_pos_freq, maha_thresh=maha_thresh))
return np.array(bools)
def maha_test_pseudorange_rate(self, x, P, meas, kind, maha_thresh=.999):
bools = []
for i, m in enumerate(meas):
z, R, sat_pos_vel = parse_prr(m)
bools.append(self.filter.maha_test(x, P, kind, z, R, extra_args=sat_pos_vel, maha_thresh=maha_thresh))
return np.array(bools)
if __name__ == "__main__":
LocKalman.generate_code(N=4)

13
selfdrive/locationd/kalman/compute_pos.c → selfdrive/locationd/kalman/templates/compute_pos.c
View File

@ -1,13 +1,15 @@
#include <eigen3/Eigen/QR>
#include <eigen3/Eigen/Dense>
#include <iostream>
#include <Eigen/QR>
#include <Eigen/Dense>
#include <iostream>
typedef Eigen::Matrix<double, KDIM*2, 3, Eigen::RowMajor> R3M;
typedef Eigen::Matrix<double, KDIM*2, 1> R1M;
typedef Eigen::Matrix<double, 3, 1> O1M;
typedef Eigen::Matrix<double, 3, 3, Eigen::RowMajor> M3D;
extern "C" {
void gauss_newton(double *in_x, double *in_poses, double *in_img_positions) {
double res[KDIM*2] = {0};
double jac[KDIM*6] = {0};
@ -28,7 +30,7 @@ void gauss_newton(double *in_x, double *in_poses, double *in_img_positions) {
void compute_pos(double *to_c, double *poses, double *img_positions, double *param, double *pos) {
param[0] = img_positions[KDIM*2-2];
param[1] = img_positions[KDIM*2-1];
param[1] = img_positions[KDIM*2-1];
param[2] = 0.1;
gauss_newton(param, poses, img_positions);
@ -49,3 +51,4 @@ void compute_pos(double *to_c, double *poses, double *img_positions, double *par
ecef_output = rot*ecef_output + ecef_offset;
memcpy(pos, ecef_output.data(), 3 * sizeof(double));
}
}

0
selfdrive/locationd/kalman/ekf_c.c → selfdrive/locationd/kalman/templates/ekf_c.c
View File

10
selfdrive/locationd/kalman/feature_handler.c → selfdrive/locationd/kalman/templates/feature_handler.c
View File

@ -1,3 +1,4 @@
extern "C"{
bool sane(double track [K + 1][5]) {
double diffs_x [K-1];
double diffs_y [K-1];
@ -14,7 +15,7 @@ bool sane(double track [K + 1][5]) {
(diffs_y[i] > 2*diffs_y[i-1] or
diffs_y[i] < .5*diffs_y[i-1]))){
return false;
}
}
}
return true;
}
@ -40,9 +41,9 @@ void merge_features(double *tracks, double *features, long long *empty_idxs) {
track_arr[match][0][3] = 1;
if (sane(track_arr[match])){
// label valid
track_arr[match][0][4] = 1;
track_arr[match][0][4] = 1;
}
}
}
} else {
// gen new track with this feature
track_arr[empty_idxs[empty_idx]][0][0] = 1;
@ -50,7 +51,8 @@ void merge_features(double *tracks, double *features, long long *empty_idxs) {
track_arr[empty_idxs[empty_idx]][0][2] = 1;
memcpy(track_arr[empty_idxs[empty_idx]][1], feature_arr[i], 5 * sizeof(double));
empty_idx = empty_idx + 1;
}
}
}
memcpy(tracks, track_arr, (K+1) * 6000 * 5 * sizeof(double));
}
}

2
selfdrive/locationd/locationd_yawrate.cc
View File

@ -3,7 +3,7 @@
#include <capnp/message.h>
#include <capnp/serialize-packed.h>
#include <eigen3/Eigen/Dense>
#include <Eigen/Dense>
#include "cereal/gen/cpp/log.capnp.h"

2
selfdrive/locationd/locationd_yawrate.h
View File

@ -1,6 +1,6 @@
#pragma once
#include <eigen3/Eigen/Dense>
#include <Eigen/Dense>
#include "cereal/gen/cpp/log.capnp.h"
#define DEGREES_TO_RADIANS 0.017453292519943295