Loc kf whitespace, fix phone accelerometer covariance
Willem Melching
parent
2b5a8fd7af
commit
965a9ae042
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@ -1,7 +1,5 @@
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#!/usr/bin/env python3
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import os
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import numpy as np
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import sympy as sp
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@ -11,7 +9,6 @@ from selfdrive.locationd.kalman.helpers.lst_sq_computer import LstSqComputer
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from selfdrive.locationd.kalman.helpers.sympy_helpers import (euler_rotate,
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quat_matrix_r,
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quat_rotate)
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#from laika.constants import EARTH_GM
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EARTH_GM = 3.986005e14 # m^3/s^2 (gravitational constant * mass of earth)
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@ -36,20 +33,20 @@ def parse_pr(m):
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class States():
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ECEF_POS = slice(0,3) # x, y and z in ECEF in meters
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ECEF_ORIENTATION = slice(3,7) # quat for pose of phone in ecef
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ECEF_VELOCITY = slice(7,10) # ecef velocity in m/s
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ANGULAR_VELOCITY = slice(10, 13) # roll, pitch and yaw rates in device frame in radians/s
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CLOCK_BIAS = slice(13, 14) # clock bias in light-meters,
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CLOCK_DRIFT = slice(14, 15) # clock drift in light-meters/s,
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GYRO_BIAS = slice(15, 18) # roll, pitch and yaw biases
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ODO_SCALE = slice(18, 19) # odometer scale
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ACCELERATION = slice(19, 22) # Acceleration in device frame in m/s**2
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FOCAL_SCALE = slice(22, 23) # focal length scale
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IMU_OFFSET = slice(23,26) # imu offset angles in radians
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GLONASS_BIAS = slice(26,27) # GLONASS bias in m expressed as bias + freq_num*freq_slope
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GLONASS_FREQ_SLOPE = slice(27, 28) # GLONASS bias in m expressed as bias + freq_num*freq_slope
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CLOCK_ACCELERATION = slice(28, 29) # clock acceleration in light-meters/s**2,
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ECEF_POS = slice(0, 3) # x, y and z in ECEF in meters
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ECEF_ORIENTATION = slice(3, 7) # quat for pose of phone in ecef
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ECEF_VELOCITY = slice(7, 10) # ecef velocity in m/s
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ANGULAR_VELOCITY = slice(10, 13) # roll, pitch and yaw rates in device frame in radians/s
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CLOCK_BIAS = slice(13, 14) # clock bias in light-meters,
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CLOCK_DRIFT = slice(14, 15) # clock drift in light-meters/s,
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GYRO_BIAS = slice(15, 18) # roll, pitch and yaw biases
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ODO_SCALE = slice(18, 19) # odometer scale
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ACCELERATION = slice(19, 22) # Acceleration in device frame in m/s**2
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FOCAL_SCALE = slice(22, 23) # focal length scale
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IMU_OFFSET = slice(23, 26) # imu offset angles in radians
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GLONASS_BIAS = slice(26, 27) # GLONASS bias in m expressed as bias + freq_num*freq_slope
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GLONASS_FREQ_SLOPE = slice(27, 28) # GLONASS bias in m expressed as bias + freq_num*freq_slope
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CLOCK_ACCELERATION = slice(28, 29) # clock acceleration in light-meters/s**2,
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class LocKalman():
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@ -69,33 +66,33 @@ class LocKalman():
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# state covariance
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P_initial = np.diag([10000**2, 10000**2, 10000**2,
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10**2, 10**2, 10**2,
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10**2, 10**2, 10**2,
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1**2, 1**2, 1**2,
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(200000)**2, (100)**2,
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0.05**2, 0.05**2, 0.05**2,
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0.02**2,
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1**2, 1**2, 1**2,
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0.01**2,
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(0.01)**2, (0.01)**2, (0.01)**2,
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10**2, 1**2,
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0.05**2])
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10**2, 10**2, 10**2,
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10**2, 10**2, 10**2,
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1**2, 1**2, 1**2,
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(200000)**2, (100)**2,
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0.05**2, 0.05**2, 0.05**2,
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0.02**2,
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1**2, 1**2, 1**2,
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0.01**2,
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(0.01)**2, (0.01)**2, (0.01)**2,
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10**2, 1**2,
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0.05**2])
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# process noise
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Q = np.diag([0.03**2, 0.03**2, 0.03**2,
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0.0**2, 0.0**2, 0.0**2,
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0.0**2, 0.0**2, 0.0**2,
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0.1**2, 0.1**2, 0.1**2,
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(.1)**2, (0.0)**2,
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(0.005/100)**2, (0.005/100)**2, (0.005/100)**2,
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(0.02/100)**2,
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3**2, 3**2, 3**2,
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0.001**2,
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(0.05/60)**2, (0.05/60)**2, (0.05/60)**2,
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(.1)**2, (.01)**2,
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0.005**2])
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0.0**2, 0.0**2, 0.0**2,
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0.0**2, 0.0**2, 0.0**2,
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0.1**2, 0.1**2, 0.1**2,
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(.1)**2, (0.0)**2,
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(0.005 / 100)**2, (0.005 / 100)**2, (0.005 / 100)**2,
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(0.02 / 100)**2,
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3**2, 3**2, 3**2,
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0.001**2,
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(0.05 / 60)**2, (0.05 / 60)**2, (0.05 / 60)**2,
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(.1)**2, (.01)**2,
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0.005**2])
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maha_test_kinds = [ObservationKind.ORB_FEATURES] #, ObservationKind.PSEUDORANGE, ObservationKind.PSEUDORANGE_RATE]
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maha_test_kinds = [ObservationKind.ORB_FEATURES] # , ObservationKind.PSEUDORANGE, ObservationKind.PSEUDORANGE_RATE]
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dim_augment = 7
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dim_augment_err = 6
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@ -116,20 +113,20 @@ class LocKalman():
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# state variables
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state_sym = sp.MatrixSymbol('state', dim_state, 1)
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state = sp.Matrix(state_sym)
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x,y,z = state[0:3,:]
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q = state[3:7,:]
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v = state[7:10,:]
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x, y, z = state[0:3, :]
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q = state[3:7, :]
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v = state[7:10, :]
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vx, vy, vz = v
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omega = state[10:13,:]
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omega = state[10:13, :]
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vroll, vpitch, vyaw = omega
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cb, cd = state[13:15,:]
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roll_bias, pitch_bias, yaw_bias = state[15:18,:]
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odo_scale = state[18,:]
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acceleration = state[19:22,:]
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focal_scale = state[22,:]
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imu_angles= state[23:26,:]
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glonass_bias, glonass_freq_slope = state[26:28,:]
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ca = state[28,0]
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cb, cd = state[13:15, :]
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roll_bias, pitch_bias, yaw_bias = state[15:18, :]
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odo_scale = state[18, :]
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acceleration = state[19:22, :]
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focal_scale = state[22, :]
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imu_angles = state[23:26, :]
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glonass_bias, glonass_freq_slope = state[26:28, :]
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ca = state[28, 0]
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dt = sp.Symbol('dt')
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@ -140,88 +137,84 @@ class LocKalman():
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# A New Quaternion-Based Kalman Filter for
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# Real-Time Attitude Estimation Using the Two-Step
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# Geometrically-Intuitive Correction Algorithm
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A = 0.5*sp.Matrix([[0, -vroll, -vpitch, -vyaw],
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[vroll, 0, vyaw, -vpitch],
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[vpitch, -vyaw, 0, vroll],
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[vyaw, vpitch, -vroll, 0]])
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A = 0.5 * sp.Matrix([[0, -vroll, -vpitch, -vyaw],
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[vroll, 0, vyaw, -vpitch],
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[vpitch, -vyaw, 0, vroll],
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[vyaw, vpitch, -vroll, 0]])
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q_dot = A * q
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# Time derivative of the state as a function of state
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state_dot = sp.Matrix(np.zeros((dim_state, 1)))
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state_dot[:3,:] = v
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state_dot[3:7,:] = q_dot
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state_dot[7:10,0] = quat_rot * acceleration
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state_dot[13,0] = cd
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state_dot[14,0] = ca
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state_dot[:3, :] = v
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state_dot[3:7, :] = q_dot
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state_dot[7:10, 0] = quat_rot * acceleration
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state_dot[13, 0] = cd
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state_dot[14, 0] = ca
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# Basic descretization, 1st order intergrator
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# Can be pretty bad if dt is big
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f_sym = state + dt*state_dot
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f_sym = state + dt * state_dot
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state_err_sym = sp.MatrixSymbol('state_err',dim_state_err,1)
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state_err_sym = sp.MatrixSymbol('state_err', dim_state_err, 1)
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state_err = sp.Matrix(state_err_sym)
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quat_err = state_err[3:6,:]
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v_err = state_err[6:9,:]
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omega_err = state_err[9:12,:]
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cd_err = state_err[13,:]
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acceleration_err = state_err[18:21,:]
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ca_err = state_err[27,:]
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quat_err = state_err[3:6, :]
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v_err = state_err[6:9, :]
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omega_err = state_err[9:12, :]
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cd_err = state_err[13, :]
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acceleration_err = state_err[18:21, :]
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ca_err = state_err[27, :]
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# Time derivative of the state error as a function of state error and state
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quat_err_matrix = euler_rotate(quat_err[0], quat_err[1], quat_err[2])
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q_err_dot = quat_err_matrix * quat_rot * (omega + omega_err)
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state_err_dot = sp.Matrix(np.zeros((dim_state_err, 1)))
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state_err_dot[:3,:] = v_err
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state_err_dot[3:6,:] = q_err_dot
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state_err_dot[6:9,:] = quat_err_matrix * quat_rot * (acceleration + acceleration_err)
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state_err_dot[12,:] = cd_err
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state_err_dot[13,:] = ca_err
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f_err_sym = state_err + dt*state_err_dot
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state_err_dot[:3, :] = v_err
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state_err_dot[3:6, :] = q_err_dot
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state_err_dot[6:9, :] = quat_err_matrix * quat_rot * (acceleration + acceleration_err)
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state_err_dot[12, :] = cd_err
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state_err_dot[13, :] = ca_err
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f_err_sym = state_err + dt * state_err_dot
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# convenient indexing
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# q idxs are for quats and p idxs are for other
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q_idxs = [[3, dim_augment]] + [[dim_main + n*dim_augment + 3, dim_main + (n+1)*dim_augment] for n in range(N)]
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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)]
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p_idxs = [[0, 3]] + [[dim_augment, dim_main]] + [[dim_main + n*dim_augment , dim_main + n*dim_augment + 3] for n in range(N)]
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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)]
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q_idxs = [[3, dim_augment]] + [[dim_main + n * dim_augment + 3, dim_main + (n + 1) * dim_augment] for n in range(N)]
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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)]
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p_idxs = [[0, 3]] + [[dim_augment, dim_main]] + [[dim_main + n * dim_augment, dim_main + n * dim_augment + 3] for n in range(N)]
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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)]
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# Observation matrix modifier
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H_mod_sym = sp.Matrix(np.zeros((dim_state, dim_state_err)))
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for p_idx, p_err_idx in zip(p_idxs, p_err_idxs):
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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])
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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])
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for q_idx, q_err_idx in zip(q_idxs, q_err_idxs):
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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:]
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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:]
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# these error functions are defined so that say there
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# is a nominal x and true x:
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# true x = err_function(nominal x, delta x)
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# delta x = inv_err_function(nominal x, true x)
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nom_x = sp.MatrixSymbol('nom_x',dim_state,1)
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true_x = sp.MatrixSymbol('true_x',dim_state,1)
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delta_x = sp.MatrixSymbol('delta_x',dim_state_err,1)
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nom_x = sp.MatrixSymbol('nom_x', dim_state, 1)
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true_x = sp.MatrixSymbol('true_x', dim_state, 1)
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delta_x = sp.MatrixSymbol('delta_x', dim_state_err, 1)
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err_function_sym = sp.Matrix(np.zeros((dim_state,1)))
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err_function_sym = sp.Matrix(np.zeros((dim_state, 1)))
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for q_idx, q_err_idx in zip(q_idxs, q_err_idxs):
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delta_quat = sp.Matrix(np.ones((4)))
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delta_quat[1:,:] = sp.Matrix(0.5*delta_x[q_err_idx[0]: q_err_idx[1],:])
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err_function_sym[q_idx[0]:q_idx[1],0] = quat_matrix_r(nom_x[q_idx[0]:q_idx[1],0])*delta_quat
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delta_quat[1:, :] = sp.Matrix(0.5 * delta_x[q_err_idx[0]: q_err_idx[1], :])
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err_function_sym[q_idx[0]:q_idx[1], 0] = quat_matrix_r(nom_x[q_idx[0]:q_idx[1], 0]) * delta_quat
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for p_idx, p_err_idx in zip(p_idxs, p_err_idxs):
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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],:])
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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], :])
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inv_err_function_sym = sp.Matrix(np.zeros((dim_state_err,1)))
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inv_err_function_sym = sp.Matrix(np.zeros((dim_state_err, 1)))
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for p_idx, p_err_idx in zip(p_idxs, p_err_idxs):
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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])
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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])
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for q_idx, q_err_idx in zip(q_idxs, q_err_idxs):
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delta_quat = quat_matrix_r(nom_x[q_idx[0]:q_idx[1],0]).T*true_x[q_idx[0]:q_idx[1],0]
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inv_err_function_sym[q_err_idx[0]:q_err_idx[1],0] = sp.Matrix(2*delta_quat[1:])
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delta_quat = quat_matrix_r(nom_x[q_idx[0]:q_idx[1], 0]).T * true_x[q_idx[0]:q_idx[1], 0]
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inv_err_function_sym[q_err_idx[0]:q_err_idx[1], 0] = sp.Matrix(2 * delta_quat[1:])
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eskf_params = [[err_function_sym, nom_x, delta_x],
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[inv_err_function_sym, nom_x, true_x],
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H_mod_sym, f_err_sym, state_err_sym]
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[inv_err_function_sym, nom_x, true_x],
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H_mod_sym, f_err_sym, state_err_sym]
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#
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# Observation functions
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#
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@ -238,92 +231,95 @@ class LocKalman():
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los_x, los_y, los_z = sat_los_sym
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orb_x, orb_y, orb_z = orb_epos_sym
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h_pseudorange_sym = sp.Matrix([sp.sqrt(
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(x - sat_x)**2 +
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(y - sat_y)**2 +
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(z - sat_z)**2) +
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cb])
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h_pseudorange_sym = sp.Matrix([
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sp.sqrt(
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(x - sat_x)**2 +
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(y - sat_y)**2 +
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(z - sat_z)**2
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) + cb
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])
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h_pseudorange_glonass_sym = sp.Matrix([sp.sqrt(
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(x - sat_x)**2 +
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(y - sat_y)**2 +
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(z - sat_z)**2) +
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cb + glonass_bias + glonass_freq_slope*glonass_freq])
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h_pseudorange_glonass_sym = sp.Matrix([
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sp.sqrt(
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(x - sat_x)**2 +
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(y - sat_y)**2 +
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(z - sat_z)**2
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) + cb + glonass_bias + glonass_freq_slope * glonass_freq
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])
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los_vector = (sp.Matrix(sat_pos_vel_sym[0:3]) - sp.Matrix([x, y, z]))
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los_vector = los_vector / sp.sqrt(los_vector[0]**2 + los_vector[1]**2 + los_vector[2]**2)
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h_pseudorange_rate_sym = sp.Matrix([los_vector[0]*(sat_vx - vx) +
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los_vector[1]*(sat_vy - vy) +
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los_vector[2]*(sat_vz - vz) +
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cd])
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h_pseudorange_rate_sym = sp.Matrix([los_vector[0] * (sat_vx - vx) +
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los_vector[1] * (sat_vy - vy) +
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los_vector[2] * (sat_vz - vz) +
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cd])
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imu_rot = euler_rotate(*imu_angles)
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h_gyro_sym = imu_rot*sp.Matrix([vroll + roll_bias,
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vpitch + pitch_bias,
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vyaw + yaw_bias])
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h_gyro_sym = imu_rot * sp.Matrix([vroll + roll_bias,
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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])
|
||||
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 = sp.sqrt(vx**2 + vy**2 + vz**2)
|
||||
h_speed_sym = sp.Matrix([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_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]]
|
||||
[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
|
||||
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)))
|
||||
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_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])
|
||||
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]
|
||||
idx = dim_main + n * dim_augment
|
||||
err_idx = dim_main_err + n * dim_augment_err # FIXME: Why is this not used?
|
||||
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])
|
||||
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])
|
||||
|
|
@ -337,9 +333,9 @@ class LocKalman():
|
|||
|
||||
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.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])}
|
||||
|
||||
|
|
@ -347,8 +343,8 @@ class LocKalman():
|
|||
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
|
||||
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)
|
||||
|
|
@ -379,15 +375,15 @@ class LocKalman():
|
|||
|
||||
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
|
||||
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)
|
||||
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')
|
||||
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):
|
||||
|
|
@ -424,15 +420,15 @@ class LocKalman():
|
|||
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])
|
||||
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
|
||||
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])
|
||||
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):
|
||||
|
|
@ -440,7 +436,7 @@ class LocKalman():
|
|||
dim = obs_noise.shape[0]
|
||||
R = np.zeros((n, dim, dim))
|
||||
for i in range(n):
|
||||
R[i,:,:] = obs_noise
|
||||
R[i, :, :] = obs_noise
|
||||
return R
|
||||
|
||||
def predict_and_update_pseudorange(self, meas, t, kind):
|
||||
|
|
@ -449,12 +445,11 @@ class LocKalman():
|
|||
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
|
||||
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))
|
||||
|
|
@ -462,61 +457,63 @@ class LocKalman():
|
|||
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
|
||||
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]
|
||||
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])
|
||||
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])
|
||||
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]
|
||||
z = trans[:, :3]
|
||||
R = np.zeros((len(trans), 3, 3))
|
||||
for i, _ in enumerate(z):
|
||||
R[i,:,:] = np.diag(trans[i,3:]**2)
|
||||
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]
|
||||
z = rot[:, :3]
|
||||
R = np.zeros((len(rot), 3, 3))
|
||||
for i, _ in enumerate(z):
|
||||
R[i,:,:] = np.diag(rot[i,3:]**2)
|
||||
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)
|
||||
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]
|
||||
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):
|
||||
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:]
|
||||
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])
|
||||
# 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))
|
||||
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)
|
||||
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
|
||||
|
|
@ -524,8 +521,8 @@ class LocKalman():
|
|||
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)
|
||||
|
|
@ -537,7 +534,7 @@ class LocKalman():
|
|||
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]))
|
||||
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
|
||||
|
|
|
|||
Loading…
Reference in New Issue