LIDAR-Based Relative Position and Attitude Filtering for Unknown Targets

doi:10.16182/j.issn1004731x.joss.201705023

Abstract

Abstract: A LIDAR-Based Extended Kalman Filter (EKF) for relative position and attitude estimation of unknowns target was proposed. The relative position and attitude between the target and a servicing spacecraft was solved by the Iterative Closet Point (ICP) using LIDAR point cloud data, which served as the EKF's measurement input. The system states of EKF include the relative attitude, angular velocity, inertia ratios, relative position, relative velocity, and the position/attitude of target measurement reference frame with respect to target principle frame. The proposed filter estimated the relative position and attitude as well as the unknown parameters of the target. To improve the confidence of numerical simulation, geomagic was used to simulate the point cloud data of LIDAR. A simulation based on Matlab verifies the proposed algorithm.

Key words: LIDAR, relative position and attitude estimation, unknown object, vision-based relative navigation, extended Kalman filter

CLC Number:

TP391.9

Song Liang, Li Zhi, Ma Xingrui. LIDAR-Based Relative Position and Attitude Filtering for Unknown Targets[J]. Journal of System Simulation, 2017, 29(5): 1103-1111.

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