Li Peng, Wang Mao, Zhang Zhao, Zhang Bing, Wang Yankun
Appl Opt. 2024 Mar 10;63(8):1952-1960. doi: 10.1364/AO.517023.
The relative attitude estimation between chasers and uncooperative targets is an important prerequisite for executing in orbit service (OOS) tasks. Only by efficiently obtaining relative pose parameters can chasers design close-range rendezvous trajectories close to uncooperative targets. The focus of this article is on active systems, such as TOF cameras or LIDAR. This paper proposes an attitude estimation scheme to obtain relative attitude parameters between uncooperative targets. This scheme utilizes LIDAR to obtain three-dimensional point clouds of non-cooperative targets, extracts key points and simplifies the number of point clouds through joint farthest point sampling and point cloud feature analysis, and then uses point fast feature histograms (FPFHs) and robust iterative closest point algorithms to achieve point cloud registration between every two frames. Finally, a filtering framework was designed, whose scheme is an extended Kalman filter designed for updating measurements of relative position, velocity, attitude, and angular velocity estimation. The experimental results show that this method can effectively achieve point cloud registration for close range rotation and translation motion, and can estimate the motion state of the target.
追踪器与非合作目标之间的相对姿态估计是执行在轨服务（OOS）任务的重要前提。只有通过有效地获取相对位姿参数，追踪器才能设计出接近非合作目标的近距离交会轨迹。本文的重点是有源系统，如飞行时间（TOF）相机或激光雷达。本文提出了一种姿态估计方案，以获取非合作目标之间的相对姿态参数。该方案利用激光雷达获取非合作目标的三维点云，通过联合最远点采样和点云特征分析提取关键点并简化点云数量，然后使用点快速特征直方图（FPFH）和鲁棒迭代最近点算法实现每两帧之间的点云配准。最后，设计了一个滤波框架，其方案是一个扩展卡尔曼滤波器，用于更新相对位置、速度、姿态和角速度估计的测量值。实验结果表明，该方法能够有效地实现近距离旋转和平移运动的点云配准，并能估计目标的运动状态。
