LIDAR-Based Relative Position and Attitude Filtering for Unknown Targets

Authors

Song Liang, 1. China Academy of Space Technology, Qian Xuesen Laboratory of Space Technology, Beijing 100091, China; ;
Li Zhi, 1. China Academy of Space Technology, Qian Xuesen Laboratory of Space Technology, Beijing 100091, China; ;
Xingrui Ma, 2. Government of Guangdong Province, Guangzhou 510031, China;

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.

Recommended Citation

Liang, Song; Zhi, Li; and Ma, Xingrui (2020) "LIDAR-Based Relative Position and Attitude Filtering for Unknown Targets," Journal of System Simulation: Vol. 29: Iss. 5, Article 23.
DOI: 10.16182/j.issn1004731x.joss.201705023
Available at: https://dc-china-simulation.researchcommons.org/journal/vol29/iss5/23

First Page

1103

Revised Date

2015-04-27

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201705023

Last Page

1111

CLC

TP391.9

Recommended Citation

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.

DOI

10.16182/j.issn1004731x.joss.201705023