摘要
针对空间机器人在轨维修等任务中的非合作目标位姿估计问题,提出一种基于点云配准的空间非合作目标位姿估计算法。在粗配准阶段,利用复合滤波算法保留点云外形并降低点云密度,通过主成分分析法求出特征向量,建立特征向量变换关系,利用RANSAC算法检验匹配效果。在精配准阶段,使用基于改进ICP算法的精配准算法,求出旋转矩阵和平移矩阵,得到位姿估计值。采用经旋转平移变换和高斯噪声处理的6组数字卫星点云模型进行点云配准性能验证,并利用TOF相机采集的卫星缩比模型点云进行位姿估计算法验证。经验证,算法的姿态角测量误差小于0.4°,位移测量误差小于3 mm,是一种针对空间相对位姿测量问题的抗噪性能更好、鲁棒性更高的有效解决手段。
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.
作者
郭素婕
郭崇滨
GUO Sujie;GUO Chongbin(Innovation Academy for Microsatellite,Chinese Academy of Sciences,Shanghai 201304,China;University of Chinese Academy of Sciences,Beijing 100049,China;Shanghai Engineering Centre for Microsatellites,Shanghai 201304,China)
出处
《中国科学院大学学报（中英文）》
CAS
CSCD
北大核心
2024年第5期677-686,共10页
Journal of University of Chinese Academy of Sciences
基金
中国科学院青年创新促进会人才项目(2019292)
上海市青年科技启明星项目(19QA1408400)资助。
关键词
空间非合作目标
点云配准
相对位姿估计
TOF相机
spatial non-cooperative targets
point cloud registration
pose estimation
TOF camera
