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openpilot v0.8.12 release

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Vehicle Researcher
2021-12-14 04:51:54 +00:00
parent ce9e739428
commit bc7d21687a
274 changed files with 3293 additions and 21528 deletions
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selfdrive/locationd/models/live_kf.cc
+21 -2
View File
@@ -28,11 +28,14 @@ std::vector<Eigen::Map<MatrixXdr>> get_vec_mapmat(std::vector<MatrixXdr>& mat_ve
}
LiveKalman::LiveKalman() {
this->dim_state = 26;
this->dim_state_err = 25;
this->dim_state = live_initial_x.rows();
this->dim_state_err = live_initial_P_diag.rows();
this->initial_x = live_initial_x;
this->initial_P = live_initial_P_diag.asDiagonal();
this->fake_gps_pos_cov = live_fake_gps_pos_cov_diag.asDiagonal();
this->fake_gps_vel_cov = live_fake_gps_vel_cov_diag.asDiagonal();
this->reset_orientation_P = live_reset_orientation_diag.asDiagonal();
this->Q = live_Q_diag.asDiagonal();
for (auto& pair : live_obs_noise_diag) {
this->obs_noise[pair.first] = pair.second.asDiagonal();
@@ -87,6 +90,10 @@ std::optional<Estimate> LiveKalman::predict_and_observe(double t, int kind, std:
return r;
}
void LiveKalman::predict(double t) {
this->filter->predict(t);
}
Eigen::VectorXd LiveKalman::get_initial_x() {
return this->initial_x;
}
@@ -95,6 +102,18 @@ MatrixXdr LiveKalman::get_initial_P() {
return this->initial_P;
}
MatrixXdr LiveKalman::get_fake_gps_pos_cov() {
return this->fake_gps_pos_cov;
}
MatrixXdr LiveKalman::get_fake_gps_vel_cov() {
return this->fake_gps_vel_cov;
}
MatrixXdr LiveKalman::get_reset_orientation_P() {
return this->reset_orientation_P;
}
MatrixXdr LiveKalman::H(VectorXd in) {
assert(in.size() == 6);
Matrix<double, 3, 6, Eigen::RowMajor> res;
selfdrive/locationd/models/live_kf.h
+7
View File
@@ -36,9 +36,13 @@ public:
std::optional<Estimate> predict_and_update_odo_speed(std::vector<Eigen::VectorXd> speed, double t, int kind);
std::optional<Estimate> predict_and_update_odo_trans(std::vector<Eigen::VectorXd> trans, double t, int kind);
std::optional<Estimate> predict_and_update_odo_rot(std::vector<Eigen::VectorXd> rot, double t, int kind);
void predict(double t);
Eigen::VectorXd get_initial_x();
MatrixXdr get_initial_P();
MatrixXdr get_fake_gps_pos_cov();
MatrixXdr get_fake_gps_vel_cov();
MatrixXdr get_reset_orientation_P();
MatrixXdr H(Eigen::VectorXd in);
@@ -52,6 +56,9 @@ private:
Eigen::VectorXd initial_x;
MatrixXdr initial_P;
MatrixXdr fake_gps_pos_cov;
MatrixXdr fake_gps_vel_cov;
MatrixXdr reset_orientation_P;
MatrixXdr Q; // process noise
std::unordered_map<int, MatrixXdr> obs_noise;
};
selfdrive/locationd/models/live_kf.py
+19 -32
View File
@@ -26,10 +26,8 @@ class States():
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
ACC_BIAS = slice(23, 26)
ACCELERATION = slice(16, 19) # Acceleration in device frame in m/s**2
ACC_BIAS = slice(19, 22) # Acceletometer bias in m/s**2
# Error-state has different slices because it is an ESKF
ECEF_POS_ERR = slice(0, 3)
@@ -37,10 +35,8 @@ class States():
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)
ACC_BIAS_ERR = slice(22, 25)
ACCELERATION_ERR = slice(15, 18)
ACC_BIAS_ERR = slice(18, 21)
class LiveKalman():
@@ -51,38 +47,37 @@ class LiveKalman():
0, 0, 0,
0, 0, 0,
0, 0, 0,
1,
0, 0, 0,
0, 0, 0,
0, 0, 0])
# state covariance
initial_P_diag = np.array([1e3**2, 1e3**2, 1e3**2,
0.5**2, 0.5**2, 0.5**2,
initial_P_diag = np.array([10**2, 10**2, 10**2,
0.01**2, 0.01**2, 0.01**2,
10**2, 10**2, 10**2,
1**2, 1**2, 1**2,
1**2, 1**2, 1**2,
0.02**2,
100**2, 100**2, 100**2,
0.01**2, 0.01**2, 0.01**2,
0.01**2, 0.01**2, 0.01**2])
# state covariance when resetting midway in a segment
reset_orientation_diag = np.array([1**2, 1**2, 1**2])
# fake observation covariance, to ensure the uncertainty estimate of the filter is under control
fake_gps_pos_cov_diag = np.array([1000**2, 1000**2, 1000**2])
fake_gps_vel_cov_diag = np.array([10**2, 10**2, 10**2])
# process noise
Q_diag = np.array([0.03**2, 0.03**2, 0.03**2,
0.001**2, 0.001**2, 0.001**2,
0.01**2, 0.01**2, 0.01**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,
0.005**2, 0.005**2, 0.005**2])
obs_noise_diag = {ObservationKind.ODOMETRIC_SPEED: np.array([0.2**2]),
ObservationKind.PHONE_GYRO: np.array([0.025**2, 0.025**2, 0.025**2]),
obs_noise_diag = {ObservationKind.PHONE_GYRO: np.array([0.025**2, 0.025**2, 0.025**2]),
ObservationKind.PHONE_ACCEL: np.array([.5**2, .5**2, .5**2]),
ObservationKind.CAMERA_ODO_ROTATION: np.array([0.05**2, 0.05**2, 0.05**2]),
ObservationKind.IMU_FRAME: np.array([0.05**2, 0.05**2, 0.05**2]),
ObservationKind.NO_ROT: np.array([0.005**2, 0.005**2, 0.005**2]),
ObservationKind.NO_ACCEL: np.array([0.05**2, 0.05**2, 0.05**2]),
ObservationKind.ECEF_POS: np.array([5**2, 5**2, 5**2]),
@@ -105,7 +100,6 @@ class LiveKalman():
vroll, vpitch, vyaw = omega
roll_bias, pitch_bias, yaw_bias = state[States.GYRO_BIAS, :]
acceleration = state[States.ACCELERATION, :]
imu_angles = state[States.IMU_OFFSET, :]
acc_bias = state[States.ACC_BIAS, :]
dt = sp.Symbol('dt')
@@ -140,7 +134,6 @@ class LiveKalman():
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)
@@ -183,7 +176,6 @@ class LiveKalman():
#
# Observation functions
#
# imu_rot = euler_rotate(*imu_angles)
h_gyro_sym = sp.Matrix([
vroll + roll_bias,
vpitch + pitch_bias,
@@ -194,19 +186,12 @@ class LiveKalman():
h_acc_sym = (gravity + acceleration + acc_bias)
h_acc_stationary_sym = acceleration
h_phone_rot_sym = sp.Matrix([vroll, vpitch, vyaw])
speed = sp.sqrt(vx**2 + vy**2 + vz**2 + 1e-6)
h_speed_sym = sp.Matrix([speed])
h_pos_sym = sp.Matrix([x, y, z])
h_vel_sym = sp.Matrix([vx, vy, vz])
h_orientation_sym = q
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],
obs_eqs = [[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],
@@ -214,12 +199,11 @@ class LiveKalman():
[h_orientation_sym, ObservationKind.ECEF_ORIENTATION_FROM_GPS, 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_acc_stationary_sym, ObservationKind.NO_ACCEL, None]]
# this returns a sympy routine for the jacobian of the observation function of the local vel
in_vec = sp.MatrixSymbol('in_vec', 6, 1) # roll, pitch, yaw, vx, vy, vz
h = euler_rotate(in_vec[0], in_vec[1], in_vec[2]).T*(sp.Matrix([in_vec[3], in_vec[4], in_vec[5]]))
h = euler_rotate(in_vec[0], in_vec[1], in_vec[2]).T * (sp.Matrix([in_vec[3], in_vec[4], in_vec[5]]))
extra_routines = [('H', h.jacobian(in_vec), [in_vec])]
gen_code(generated_dir, name, f_sym, dt, state_sym, obs_eqs, dim_state, dim_state_err, eskf_params, extra_routines=extra_routines)
@@ -241,6 +225,9 @@ class LiveKalman():
live_kf_header += f"static const Eigen::VectorXd live_initial_x = {numpy2eigenstring(LiveKalman.initial_x)};\n"
live_kf_header += f"static const Eigen::VectorXd live_initial_P_diag = {numpy2eigenstring(LiveKalman.initial_P_diag)};\n"
live_kf_header += f"static const Eigen::VectorXd live_fake_gps_pos_cov_diag = {numpy2eigenstring(LiveKalman.fake_gps_pos_cov_diag)};\n"
live_kf_header += f"static const Eigen::VectorXd live_fake_gps_vel_cov_diag = {numpy2eigenstring(LiveKalman.fake_gps_vel_cov_diag)};\n"
live_kf_header += f"static const Eigen::VectorXd live_reset_orientation_diag = {numpy2eigenstring(LiveKalman.reset_orientation_diag)};\n"
live_kf_header += f"static const Eigen::VectorXd live_Q_diag = {numpy2eigenstring(LiveKalman.Q_diag)};\n"
live_kf_header += "static const std::unordered_map<int, Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>> live_obs_noise_diag = {\n"
for kind, noise in LiveKalman.obs_noise_diag.items():