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