基于TOF相机的超近程空间非合作目标的位姿估计

Pose Estimation of Ultra-Short Range Space Non-Cooperative Target Based on TOF Camera

基于TOF(time of flight)相机设计一种融合目标二维灰度信息与三维点云信息的位姿估计算法框架,解算追踪星与非合作目标之间的相对位姿.该算法框架使用TOF相机获取的目标的灰度图像和三维点云数据,采用基于弧段的椭圆提取方法和ICP(iterative closest point)点云迭代方法求解非合作目标的相对位姿信息.融合了基于灰度图像的图像处理算法与基于点云的位姿优化迭代算法提高了位姿解算算法的精度与鲁棒性,通过地面测试试验验证了所提算法框架的性能.在轨试验表明本文所提算法框架可以稳定有效地解算超近程空间非合作目标的相对位姿,为追踪星GNC分系统提供可靠导航信息且TOF相机输出相对位姿精度优于2°、5 cm.

Based on TOF(time of flight)camera,this paper designs a pose estimation algorithm framework integrating two-dimensional gray information and three-dimensional point cloud information of the target to solve the relative pose between the tracking star and the non-cooperative target.The algorithm framework uses the gray image and three-dimensional point cloud data of the target obtained by TOF camera,and uses the arc based ellipse extraction method and ICP(iterative close point)point cloud iteration method to solve the relative pose information of non-cooperative targets.This paper combines the image processing algorithm based on gray image and the pose optimization iterative algorithm based on point cloud,which improves the accuracy and robustness of the pose solution algorithm.A ground test verifies the performance of the proposed algorithm framework.The in-orbit test shows that the proposed algorithm framework can stably and effectively solve the relative pose of the ultra-short-range space non-cooperative target,provide reliable navigation information for the tracking satellite GNC subsystem,and the relative pose accuracy of TOF camera is better than 2°and 5 cm.