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138 lines
4.8 KiB
138 lines
4.8 KiB
#!/usr/bin/env python3
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import sys
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import numpy as np
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from openpilot.selfdrive.locationd.models.constants import ObservationKind
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if __name__=="__main__":
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import sympy as sp
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from rednose.helpers.ekf_sym import gen_code
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from rednose.helpers.sympy_helpers import euler_rotate, rot_to_euler
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else:
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from rednose.helpers.ekf_sym_pyx import EKF_sym_pyx
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EARTH_G = 9.81
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class States:
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NED_ORIENTATION = slice(0, 3) # roll, pitch, yaw in rad
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DEVICE_VELOCITY = slice(3, 6) # ned velocity in m/s
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ANGULAR_VELOCITY = slice(6, 9) # roll, pitch and yaw rates in rad/s
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GYRO_BIAS = slice(9, 12) # roll, pitch and yaw gyroscope biases in rad/s
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ACCELERATION = slice(12, 15) # acceleration in device frame in m/s**2
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ACCEL_BIAS = slice(15, 18) # Acceletometer bias in m/s**2
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class PoseKalman:
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name = "pose"
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# state
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initial_x = np.array([0.0, 0.0, 0.0,
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0.0, 0.0, 0.0,
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0.0, 0.0, 0.0,
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0.0, 0.0, 0.0,
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0.0, 0.0, 0.0,
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0.0, 0.0, 0.0])
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# state covariance
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initial_P = np.diag([0.01**2, 0.01**2, 0.01**2,
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10**2, 10**2, 10**2,
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1**2, 1**2, 1**2,
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1**2, 1**2, 1**2,
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100**2, 100**2, 100**2,
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0.01**2, 0.01**2, 0.01**2])
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# process noise
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Q = np.diag([0.001**2, 0.001**2, 0.001**2,
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0.01**2, 0.01**2, 0.01**2,
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0.1**2, 0.1**2, 0.1**2,
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(0.005 / 100)**2, (0.005 / 100)**2, (0.005 / 100)**2,
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3**2, 3**2, 3**2,
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0.005**2, 0.005**2, 0.005**2])
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obs_noise = {ObservationKind.PHONE_GYRO: np.array([0.025**2, 0.025**2, 0.025**2]),
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ObservationKind.PHONE_ACCEL: np.array([.5**2, .5**2, .5**2]),
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ObservationKind.CAMERA_ODO_TRANSLATION: np.array([0.5**2, 0.5**2, 0.5**2]),
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ObservationKind.CAMERA_ODO_ROTATION: np.array([0.05**2, 0.05**2, 0.05**2])}
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@staticmethod
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def generate_code(generated_dir):
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name = PoseKalman.name
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dim_state = PoseKalman.initial_x.shape[0]
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dim_state_err = PoseKalman.initial_P.shape[0]
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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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roll, pitch, yaw = state[States.NED_ORIENTATION, :]
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velocity = state[States.DEVICE_VELOCITY, :]
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angular_velocity = state[States.ANGULAR_VELOCITY, :]
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vroll, vpitch, vyaw = angular_velocity
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gyro_bias = state[States.GYRO_BIAS, :]
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acceleration = state[States.ACCELERATION, :]
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acc_bias = state[States.ACCEL_BIAS, :]
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dt = sp.Symbol('dt')
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ned_from_device = euler_rotate(roll, pitch, yaw)
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device_from_ned = ned_from_device.T
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state_dot = sp.Matrix(np.zeros((dim_state, 1)))
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state_dot[States.DEVICE_VELOCITY, :] = acceleration
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f_sym = state + dt * state_dot
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device_from_device_t1 = euler_rotate(dt*vroll, dt*vpitch, dt*vyaw)
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ned_from_device_t1 = ned_from_device * device_from_device_t1
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f_sym[States.NED_ORIENTATION, :] = rot_to_euler(ned_from_device_t1)
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centripetal_acceleration = angular_velocity.cross(velocity)
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gravity = sp.Matrix([0, 0, -EARTH_G])
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h_gyro_sym = angular_velocity + gyro_bias
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h_acc_sym = device_from_ned * gravity + acceleration + centripetal_acceleration + acc_bias
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h_phone_rot_sym = angular_velocity
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h_relative_motion_sym = velocity
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obs_eqs = [
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[h_gyro_sym, ObservationKind.PHONE_GYRO, None],
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[h_acc_sym, ObservationKind.PHONE_ACCEL, None],
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[h_relative_motion_sym, ObservationKind.CAMERA_ODO_TRANSLATION, None],
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[h_phone_rot_sym, ObservationKind.CAMERA_ODO_ROTATION, None],
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]
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gen_code(generated_dir, name, f_sym, dt, state_sym, obs_eqs, dim_state, dim_state_err)
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def __init__(self, generated_dir, max_rewind_age):
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dim_state, dim_state_err = PoseKalman.initial_x.shape[0], PoseKalman.initial_P.shape[0]
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self.filter = EKF_sym_pyx(generated_dir, self.name, PoseKalman.Q, PoseKalman.initial_x, PoseKalman.initial_P,
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dim_state, dim_state_err, max_rewind_age=max_rewind_age)
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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 P(self):
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return self.filter.covs()
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@property
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def t(self):
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return self.filter.get_filter_time()
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def predict_and_observe(self, t, kind, data, obs_noise=None):
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data = np.atleast_2d(data)
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if obs_noise is None:
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obs_noise = self.obs_noise[kind]
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R = self._get_R(len(data), obs_noise)
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return self.filter.predict_and_update_batch(t, kind, data, R)
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def reset(self, t, x_init, P_init):
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self.filter.init_state(x_init, P_init, t)
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def _get_R(self, n, obs_noise):
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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, :, :] = np.diag(obs_noise)
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return R
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if __name__ == "__main__":
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generated_dir = sys.argv[2]
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PoseKalman.generate_code(generated_dir)
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