Willem Melching
5 years ago
parent
8611b1b648
commit
4bfe88a31a
2 changed files with 0 additions and 208 deletions
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#!/usr/bin/env python3 |
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import numpy as np |
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from selfdrive.locationd.kalman import loc_local_model |
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from selfdrive.locationd.kalman.kalman_helpers import ObservationKind |
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from selfdrive.locationd.kalman.ekf_sym import EKF_sym |
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class States(): |
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VELOCITY = slice(0,3) # device frame velocity in m/s |
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ANGULAR_VELOCITY = slice(3, 6) # roll, pitch and yaw rates in device frame in radians/s |
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GYRO_BIAS = slice(6, 9) # roll, pitch and yaw biases |
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ODO_SCALE = slice(9, 10) # odometer scale |
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ACCELERATION = slice(10, 13) # Acceleration in device frame in m/s**2 |
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class LocLocalKalman(): |
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def __init__(self): |
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x_initial = np.array([0, 0, 0, |
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0, 0, 0, |
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0, 0, 0, |
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1, |
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0, 0, 0]) |
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# state covariance |
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P_initial = np.diag([10**2, 10**2, 10**2, |
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1**2, 1**2, 1**2, |
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0.05**2, 0.05**2, 0.05**2, |
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0.02**2, |
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1**2, 1**2, 1**2]) |
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# process noise |
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Q = np.diag([0.0**2, 0.0**2, 0.0**2, |
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.01**2, .01**2, .01**2, |
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(0.005/100)**2, (0.005/100)**2, (0.005/100)**2, |
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(0.02/100)**2, |
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3**2, 3**2, 3**2]) |
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self.obs_noise = {ObservationKind.ODOMETRIC_SPEED: np.atleast_2d(0.2**2), |
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ObservationKind.PHONE_GYRO: np.diag([0.025**2, 0.025**2, 0.025**2])} |
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# MSCKF stuff |
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self.dim_state = len(x_initial) |
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self.dim_main = self.dim_state |
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name = 'loc_local' |
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loc_local_model.gen_model(name, self.dim_state) |
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# init filter |
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self.filter = EKF_sym(name, Q, x_initial, P_initial, self.dim_main, self.dim_main) |
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@property |
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def x(self): |
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return self.filter.state() |
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@property |
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def t(self): |
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return self.filter.filter_time |
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@property |
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def P(self): |
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return self.filter.covs() |
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def predict(self, t): |
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if self.t: |
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# Does NOT modify filter state |
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return self.filter._predict(self.x, self.P, t - self.t)[0] |
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else: |
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raise RuntimeError("Request predict on filter with uninitialized time") |
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def rts_smooth(self, estimates): |
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return self.filter.rts_smooth(estimates, norm_quats=True) |
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def init_state(self, state, covs_diag=None, covs=None, filter_time=None): |
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if covs_diag is not None: |
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P = np.diag(covs_diag) |
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elif covs is not None: |
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P = covs |
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else: |
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P = self.filter.covs() |
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self.filter.init_state(state, P, filter_time) |
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def predict_and_observe(self, t, kind, data): |
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if len(data) > 0: |
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data = np.atleast_2d(data) |
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if kind == ObservationKind.CAMERA_ODO_TRANSLATION: |
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r = self.predict_and_update_odo_trans(data, t, kind) |
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elif kind == ObservationKind.CAMERA_ODO_ROTATION: |
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r = self.predict_and_update_odo_rot(data, t, kind) |
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elif kind == ObservationKind.ODOMETRIC_SPEED: |
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r = self.predict_and_update_odo_speed(data, t, kind) |
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else: |
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r = self.filter.predict_and_update_batch(t, kind, data, self.get_R(kind, len(data))) |
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return r |
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def get_R(self, kind, n): |
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obs_noise = self.obs_noise[kind] |
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dim = obs_noise.shape[0] |
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R = np.zeros((n, dim, dim)) |
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for i in range(n): |
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R[i,:,:] = obs_noise |
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return R |
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def predict_and_update_odo_speed(self, speed, t, kind): |
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z = np.array(speed) |
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R = np.zeros((len(speed), 1, 1)) |
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for i, _ in enumerate(z): |
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R[i,:,:] = np.diag([0.2**2]) |
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return self.filter.predict_and_update_batch(t, kind, z, R) |
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def predict_and_update_odo_trans(self, trans, t, kind): |
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z = trans[:,:3] |
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R = np.zeros((len(trans), 3, 3)) |
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for i, _ in enumerate(z): |
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R[i,:,:] = np.diag(trans[i,3:]**2) |
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return self.filter.predict_and_update_batch(t, kind, z, R) |
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def predict_and_update_odo_rot(self, rot, t, kind): |
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z = rot[:,:3] |
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R = np.zeros((len(rot), 3, 3)) |
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for i, _ in enumerate(z): |
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R[i,:,:] = np.diag(rot[i,3:]**2) |
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return self.filter.predict_and_update_batch(t, kind, z, R) |
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if __name__ == "__main__": |
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LocLocalKalman() |
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@ -1,80 +0,0 @@ |
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import numpy as np |
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import sympy as sp |
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import os |
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from selfdrive.locationd.kalman.kalman_helpers import ObservationKind |
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from selfdrive.locationd.kalman.ekf_sym import gen_code |
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def gen_model(name, dim_state): |
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# check if rebuild is needed |
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try: |
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dir_path = os.path.dirname(__file__) |
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deps = [dir_path + '/' + 'ekf_c.c', |
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dir_path + '/' + 'ekf_sym.py', |
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dir_path + '/' + 'loc_local_model.py', |
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dir_path + '/' + 'loc_local_kf.py'] |
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outs = [dir_path + '/' + name + '.o', |
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dir_path + '/' + name + '.so', |
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dir_path + '/' + name + '.cpp'] |
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out_times = list(map(os.path.getmtime, outs)) |
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dep_times = list(map(os.path.getmtime, deps)) |
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rebuild = os.getenv("REBUILD", False) |
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if min(out_times) > max(dep_times) and not rebuild: |
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return |
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list(map(os.remove, outs)) |
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except OSError: |
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pass |
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# make functions and jacobians with sympy |
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# state variables |
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state_sym = sp.MatrixSymbol('state', dim_state, 1) |
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state = sp.Matrix(state_sym) |
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v = state[0:3,:] |
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omega = state[3:6,:] |
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vroll, vpitch, vyaw = omega |
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vx, vy, vz = v |
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roll_bias, pitch_bias, yaw_bias = state[6:9,:] |
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odo_scale = state[9,:] |
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accel = state[10:13,:] |
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dt = sp.Symbol('dt') |
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# Time derivative of the state as a function of state |
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state_dot = sp.Matrix(np.zeros((dim_state, 1))) |
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state_dot[:3,:] = accel |
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# Basic descretization, 1st order intergrator |
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# Can be pretty bad if dt is big |
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f_sym = sp.Matrix(state + dt*state_dot) |
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# |
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# Observation functions |
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# |
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# extra args |
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#imu_rot = euler_rotate(*imu_angles) |
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#h_gyro_sym = imu_rot*sp.Matrix([vroll + roll_bias, |
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# vpitch + pitch_bias, |
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# vyaw + yaw_bias]) |
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h_gyro_sym = sp.Matrix([vroll + roll_bias, |
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vpitch + pitch_bias, |
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vyaw + yaw_bias]) |
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speed = vx**2 + vy**2 + vz**2 |
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h_speed_sym = sp.Matrix([sp.sqrt(speed)*odo_scale]) |
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h_relative_motion = sp.Matrix(v) |
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h_phone_rot_sym = sp.Matrix([vroll, |
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vpitch, |
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vyaw]) |
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obs_eqs = [[h_speed_sym, ObservationKind.ODOMETRIC_SPEED, None], |
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[h_gyro_sym, ObservationKind.PHONE_GYRO, None], |
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[h_phone_rot_sym, ObservationKind.NO_ROT, None], |
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[h_relative_motion, ObservationKind.CAMERA_ODO_TRANSLATION, None], |
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[h_phone_rot_sym, ObservationKind.CAMERA_ODO_ROTATION, None]] |
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gen_code(name, f_sym, dt, state_sym, obs_eqs, dim_state, dim_state) |
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