Aiming at pose estimation for non-cooperative targets in on-orbit maintenance operations of space robots, we propose a pose estimation algorithm based on point cloud registration. Firstly, the hybrid filtering algorithm preserves the shape of the point cloud to the greatest extent while reducing its density. Then, the principal component analysis algorithm is used to establish the eigenvector transformation. The RANSAC algorithm is employed for coarse registration, followed by the improved ICP algorithm for fine registration, which results in the estimation of the rotation matrix, translation matrix, and attitude. Experiments are carried out to evaluate the performance of the algorithm. Simulated satellite point cloud models processed by rotation-translation transformations and Gaussian noise are used to verify the point cloud registration performance. The satellite model scene collected by a TOF camera point cloud is used to validate the pose estimation algorithm. The results show that the proposed algorithm has improved anti-noise performance, and showed higher robustness compared to traditional registration algorithms, with rotation attitude angle error less than 0.4° and displacement error less than 3 mm.
GUO Sujie
,
GUO Chongbin
. A pose estimation algorithm for spatial non-cooperative targets based on point cloud registration[J]. Journal of University of Chinese Academy of Sciences, 2024
, 41(5)
: 677
-686
.
DOI: 10.7523/j.ucas.2023.027
[1] Meng G, Han L L, Zhang C F. Research progress and technical challenges of space robot[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(1):1-27. DOI:10.7527/S1000-6893.2020.23963.
[2] Brannan J, Scott N, Carignan C. Robot servicer interaction with a satellite during capture[C/OL]//International Symposium on Artificial Inte-lligence, Robotics and Automation in Space (iSAIRAS).(2018-07-12)[2022-12-21]. https://robotics.estec.esa.int/i-SAIRAS/isairas2018/Papers/Session%203b/1_brancarscott18_iSAIRAS_SUBMITTED_TO_SSPD-18-49-Brannan-Justin.pdf.
[3] 程瑞洲,黄攀峰,刘正雄,等.一种面向在轨服务的空间遥操作人机交互方法[J].宇航学报, 2021, 42(9):1187-1196. DOI:10.3873/j.issn.1000-1328.2021.09.014.
[4] Peng J Q, Xu W F, Liang B, et al. Pose measurement and motion estimation of space non-cooperative targets based on laser radar and stereo-vision fusion[J]. IEEE Sensors Journal, 2019, 19(8):3008-3019. DOI:10.1109/JSEN.2018.2889469.
[5] 刘王超,娄鑫.基于双目点云的三维物体检测准确性分析[J].中国科学院大学学报, 2022, 39(5):677-683. DOI:10.7523/j.ucas.2020.0042.
[6] 陈凤,朱洁,顾冬晴,等.基于激光成像雷达的空间非合作目标相对导航技术[J].红外与激光工程, 2016, 45(10):107-114. DOI:10.3788/IRLA201645.1030005.
[7] Besl P J, McKay N D. Method for registration of 3-D shapes[C]//Proc SPIE 1611, Sensor Fusion IV:Control Paradigms and Data Structures, 1992, 1611:586-606. DOI:10.1117/12.57955.
[8] 唐思琦,韩丛英,郭田德.基于机器学习的点集匹配算法[J].中国科学院大学学报, 2020, 37(4):450-457. DOI:10.7523/j.issn.2095-6134.2020.04.003.
[9] 王飞鹏,肖俊,王颖,等.一种基于高斯曲率的ICP改进算法[J].中国科学院大学学报, 2019, 36(5):702-708. DOI:10.7523/j.issn.2095-6134.2019.05.016.
[10] 周颖,林意.基于法向量投票的点云配准方法[J].计算机应用研究, 2022, 39(2):637-640. DOI:10.19734/j.issn.1001-3695.2021.06.0236.
[11] 陶海跻,达飞鹏.一种基于法向量的点云自动配准方法[J].中国激光, 2013, 40(8):184-189. DOI:10.3788/CJL201340.0809001.
[12] 韩一菲,杨紫骞,郑福,等.基于FPFH和法向量的改进点云配准算法[J].半导体光电, 2021, 42(4):579-584. DOI:10.16818/j.issn1001-5868.2021.04.025.
[13] 石峰源,郑循江,姜丽辉,等.基于霍夫变换的空间非合作目标点云配准算法[J].北京航空航天大学学报,2023,49(8):2071-2078. DOI:10.13700/j.bh.1001-5965.2021.0575.
[14] 卢祺,林婷婷,李程鹏,等.空间非合作目标点云聚类配准方法[J].红外与激光工程,2021,50(9):362-371. DOI:10.3788/IRLA20200431.
[15] Ding Q Y, He J. The detection of non-cooperative targets in space by using 3D point cloud[C]//2019 5th international Conference on Control, Automation and Robotics (ICCAR). April 19-22, 2019, Beijing, China. IEEE, 2019:545-549. DOI:10.1109/ICCAR.2019.8813367.
[16] Bentley J L.Multidimensional binary search trees used for associative searching[J].Communications of the ACM, 1975, 18(9):509-517. DOI:10.1145/361002.361007.
[17] Weber T, Hänsch R, Hellwich O. Automatic registration of unordered point clouds acquired by Kinect sensors using an overlap heuristic[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 102:96-109. DOI:10.1016/j.isprsjprs.2014.12.014.
[18] Huang P Z, Yang Y B, Huang T C. Real-time measurement of model attitude based on NDT and ICP[C]//4th Optics Young Scientist Summit (OYSS 2020). December 4-7, 2020. Ningbo, China. SPIE, 2021, 11781:302-308. DOI:10.1117/12.2591140.
[19] Lin S D, Peng Y P, Cao G Z. Low overlapping plant point cloud registration and splicing method based on FPFH[C]//Chinese Conference on Image and Graphics Technologies. Singapore:Springer, 2022:103-117.DOI:10.1007/978-981-19-5096-4_8.
