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IQ.Pilot Release Commit @ b6534c0

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IQ.Lvbs CI [bot]
2026-08-27 20:17:33 -05:00
commit 00f07cac48
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iqpilot/selfdrive/state_estimation/native_binding_pyx.pyx Normal file
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"""
Copyright © IQ.Lvbs, apart of Project Teal Lvbs, All Rights Reserved, licensed under https://konn3kt.com/tos
"""
import numpy as np
cimport numpy as np
cdef extern from "iqpilot/selfdrive/state_estimation/native_kernels.h":
void iq_estimator_car_predict(double *, double *, const double *, double, const double *)
void iq_estimator_car_update(double *, double *, int, const double *, const double *, double *)
void iq_estimator_pose_predict(double *, double *, const double *, double)
void iq_estimator_pose_update(double *, double *, int, const double *, const double *, double *)
def car_predict(np.ndarray[np.float64_t, ndim=1, mode="c"] state,
np.ndarray[np.float64_t, ndim=2, mode="c"] covariance,
np.ndarray[np.float64_t, ndim=2, mode="c"] process_noise,
double dt,
np.ndarray[np.float64_t, ndim=1, mode="c"] parameters):
iq_estimator_car_predict(&state[0], &covariance[0, 0], &process_noise[0, 0], dt, &parameters[0])
def car_update(np.ndarray[np.float64_t, ndim=1, mode="c"] state,
np.ndarray[np.float64_t, ndim=2, mode="c"] covariance,
int kind,
np.ndarray[np.float64_t, ndim=1, mode="c"] measurement,
np.ndarray[np.float64_t, ndim=2, mode="c"] noise):
cdef np.ndarray[np.float64_t, ndim=1, mode="c"] innovation = np.empty(measurement.size)
iq_estimator_car_update(&state[0], &covariance[0, 0], kind, &measurement[0], &noise[0, 0], &innovation[0])
return innovation
def pose_predict(np.ndarray[np.float64_t, ndim=1, mode="c"] state,
np.ndarray[np.float64_t, ndim=2, mode="c"] covariance,
np.ndarray[np.float64_t, ndim=2, mode="c"] process_noise,
double dt):
iq_estimator_pose_predict(&state[0], &covariance[0, 0], &process_noise[0, 0], dt)
def pose_update(np.ndarray[np.float64_t, ndim=1, mode="c"] state,
np.ndarray[np.float64_t, ndim=2, mode="c"] covariance,
int kind,
np.ndarray[np.float64_t, ndim=1, mode="c"] measurement,
np.ndarray[np.float64_t, ndim=2, mode="c"] noise):
cdef np.ndarray[np.float64_t, ndim=1, mode="c"] innovation = np.empty(measurement.size)
iq_estimator_pose_update(&state[0], &covariance[0, 0], kind, &measurement[0], &noise[0, 0], &innovation[0])
return innovation