Add lane ekf (#26986)

* change lane_kf pos

* add lane back here

SConstruct

@@ -387,10 +387,10 @@ rednose_config = {
 if arch != "larch64":
   rednose_config['to_build'].update({
     'loc_4': ('#selfdrive/locationd/models/loc_kf.py', True, [], rednose_deps),
+    'lane': ('#selfdrive/locationd/models/lane_kf.py', True, [], rednose_deps),
     'pos_computer_4': ('#rednose/helpers/lst_sq_computer.py', False, [], []),
     'pos_computer_5': ('#rednose/helpers/lst_sq_computer.py', False, [], []),
     'feature_handler_5': ('#rednose/helpers/feature_handler.py', False, [], []),
-    'lane': ('#xx/pipeline/lib/ekf/lane_kf.py', True, [], rednose_deps),
   })
 
 Export('rednose_config')

selfdrive/locationd/models/lane_kf.py

@@ -0,0 +1,105 @@
+#!/usr/bin/env python3
+import sys
+import numpy as np
+import sympy as sp
+
+from 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)