A Method for Estimating Motion State of Small Bodies Based on Fusion of Camera and LIDAR

Abstract

Only one vision sensor is incompetent for estimating the motion state of small body. In order to solve this problem, a small body motion state estimation method based on the fusion of camera and LIDAR was proposed. Firstly, a fused camera and LIDAR measurement model was built. By tracking image feature points with depth information, extended Kalman filter was used to estimate the spin angular velocity, spin axis direction, position and velocity of small body. Secondly, a feature fusion matrix was designed to achieve real-time updates of image feature points, point clouds, and fused feature points. Thirdly, the effectiveness of this algorithm and the impact of the number of feature points, observation height, and noise on the algorithm were analyzed. Simulation results show that the accuracy of the proposed algorithm is significantly higher than that of the spin parameter estimation algorithm of small bodies based on monocular camera.

Key words： information fusion; small body; motion state estimation; extended Kalman filtering

References

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