openpilot is an open source driver assistance system. openpilot performs the functions of Automated Lane Centering and Adaptive Cruise Control for over 200 supported car makes and models.
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#!/usr/bin/env python3
import os
import numpy as np
import sympy as sp
#import scipy.optimize as opt
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()