基于先验子图检测的失效航天器SLAM方法

SLAM method of failure spacecraft based on prior submap detecting

基于激光雷达的航天器位姿估计技术是当前在轨服务研究热点。针对失效航天器位姿估计,将通用的图优化SLAM技术应用到空间非合作目标的研究中。为解决SLAM算法在动态场景中产生累积误差问题,利用失效航天器自身运动特点,提出一种基于先验子图检测改进的SLAM算法。在该算法中,通过激光雷达和惯性测量单元分别采集失效航天器及周围环境的点云数据、服务航天器的运动信息,构建出服务场景下航天器的相对位姿图;再采用先验子图检测方法建立不连续的位姿节点间的约束关系;最后用约束信息对位姿图进行优化。仿真结果表明,相较于通用的SLAM算法的位姿估计,该方法减小了累积误差,提高了相对位姿估计精度,可以为后期的导航、控制等在轨任务提供信息。

The technology of estimating position and pose of spacecraft based on laser lidar is a research hotspot of on-orbit services. For the position and pose estimation of failure spacecraft, the technology of general graph-based optimization SLAM was applied to the research of noncooperative target in space. In order to solve the problem of accumulative error generated in the dynamic scene, an improved SLAM algorithm based on detection of prior submap according to the characteristics of failure spacecraft was proposed. In this algorithm, laser lidar and inertia measurement unit were used to sample the point cloud data of failure spacecraft as well as the environment and the movement information of service spacecraft, to construct a relative pose graph of service scenario. Then the method of detecting prior submap was used to generate the constraints of discontinuous poses. At last, the constraints information was used to optimize the result of pose graph. The simulation result shows that the method decreases the cumulative deviation and improves precision of relative position and pose estimation compared to general SLAM algorithm, providing information for on-orbit tasks such as navigation and controlling.