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128 lines
3.8 KiB
128 lines
3.8 KiB
#!/usr/bin/env python
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import numpy as np
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import loc_local_model
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from kalman_helpers import ObservationKind
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from ekf_sym import EKF_sym
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class States(object):
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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(object):
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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 xrange(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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