You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
105 lines
2.9 KiB
105 lines
2.9 KiB
#!/usr/bin/env python3
|
|
import sys
|
|
import numpy as np
|
|
import sympy as sp
|
|
|
|
from openpilot.selfdrive.locationd.models.constants import ObservationKind
|
|
from rednose.helpers.ekf_sym import gen_code, EKF_sym
|
|
|
|
|
|
class LaneKalman():
|
|
name = 'lane'
|
|
|
|
@staticmethod
|
|
def generate_code(generated_dir):
|
|
# make functions and jacobians with sympy
|
|
# state variables
|
|
dim = 6
|
|
state = sp.MatrixSymbol('state', dim, 1)
|
|
|
|
dd = sp.Symbol('dd') # WARNING: NOT TIME
|
|
|
|
# Time derivative of the state as a function of state
|
|
state_dot = sp.Matrix(np.zeros((dim, 1)))
|
|
state_dot[:3,0] = sp.Matrix(state[3:6,0])
|
|
|
|
# Basic descretization, 1st order intergrator
|
|
# Can be pretty bad if dt is big
|
|
f_sym = sp.Matrix(state) + dd*state_dot
|
|
|
|
#
|
|
# Observation functions
|
|
#
|
|
h_lane_sym = sp.Matrix(state[:3,0])
|
|
obs_eqs = [[h_lane_sym, ObservationKind.LANE_PT, None]]
|
|
gen_code(generated_dir, LaneKalman.name, f_sym, dd, state, obs_eqs, dim, dim)
|
|
|
|
def __init__(self, generated_dir, pt_std=5):
|
|
# state
|
|
# left and right lane centers in ecef
|
|
# WARNING: this is not a temporal model
|
|
# the 'time' in this kalman filter is
|
|
# the distance traveled by the vehicle,
|
|
# which should approximately be the
|
|
# distance along the lane path
|
|
# a more logical parametrization
|
|
# states 0-2 are ecef coordinates distance d
|
|
# states 3-5 is the 3d "velocity" of the
|
|
# lane in ecef (m/m).
|
|
x_initial = np.array([0,0,0,
|
|
0,0,0])
|
|
|
|
# state covariance
|
|
P_initial = np.diag([1e16, 1e16, 1e16,
|
|
1**2, 1**2, 1**2])
|
|
|
|
# process noise
|
|
Q = np.diag([0.1**2, 0.1**2, 0.1**2,
|
|
0.1**2, 0.1**2, 0.1*2])
|
|
|
|
self.dim_state = len(x_initial)
|
|
|
|
# init filter
|
|
self.filter = EKF_sym(generated_dir, self.name, Q, x_initial, P_initial, x_initial.shape[0], P_initial.shape[0])
|
|
self.obs_noise = {ObservationKind.LANE_PT: np.diag([pt_std**2]*3)}
|
|
|
|
@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):
|
|
data = np.atleast_2d(data)
|
|
return self.filter.predict_and_update_batch(t, kind, data, self.get_R(kind, len(data)))
|
|
|
|
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
|
|
|
|
|
|
if __name__ == "__main__":
|
|
generated_dir = sys.argv[2]
|
|
LaneKalman.generate_code(generated_dir)
|
|
|