空间非合作旋转目标的模型重建与位姿优化

Model reconstruction and pose optimization of non-cooperative rotating space target

针对模型未知的空间非合作旋转目标的模型重建和位姿估计问题,利用激光雷达采集的3D点云,提出一种基于位姿图优化的SLAM技术框架,以解决跟踪过程中产生的累积误差问题。首先,根据迭代最近点(Iterative Closest Point,ICP)算法计算相邻关键帧之间的相对位姿信息,通过位姿跟踪方法获得当前关键帧的位姿,由此构建跟踪航天器的相对位姿图;采用GLAROT-3D(Geometric LAndmark relations ROTation-invariant 3D)全局描述子检测闭环,并将闭环约束添加到位姿图中;最后采用基于位姿图优化的方法进行位姿调整,并更新模型点云。在仿真实验中,噪声标准差达到100 mm时,姿态测量误差小于2°;在地面实验中,姿态测量误差小于2.5°,并较好地重建了目标的点云模型,算法的精度及抗噪声能力基本满足非合作目标相对位姿测量的任务需求。

For the model reconstruction and pose estimation of non-cooperative rotating space targets with unknown model,the technology of graph-based optimization SLAM was applied to reduce the cumulative error in the pose tracking process by using 3D point clouds acquired through LiDAR.First,the relative pose between adjacent key frames was calculated by the Iterative Closest Point(ICP)algorithm,and the pose of the current key frame was obtained by the pose tracking method,thereby constructing the pose graph of the chaser spacecraft.Meanwhile,the global 3D signature GLAROT-3D(Geometric LAndmark relations ROTation-invariant 3D)was used to detect the loop closure,and adding the closed-loop constraint to the pose graph.Finally,the method based on pose graph optimization was used to adjust the pose and update the model point cloud.Experimental results show that in the simulation test,when the noise amplitude reaches 100mm,the attitude measurement error is less than 2°.In the field experiment,the attitude measurement error is less than 2.5°,and the target point cloud model is well reconstructed.Hence,the accuracy and the anti-noise ability of the proposed method can satisfy the mission requirements for the relative pose measurement of non-cooperative target.