一种面向空间非合作目标位姿测量应用的三维点云滤波算法

针对激光位姿敏感器获得的原始点云有噪声和直接参与解算消耗星上计算资源过大问题，给出一种适用于空间非合作目标位姿测量的点云滤波和特征提取算法。应用仿真的方法分别验证了算法滤除空间随机噪声和点云降采样的有效性，验证了特征点对目标位姿变化和高斯测量噪声的鲁棒性。在非合作目标绕飞、抵近、捕获全物理试验平台上，以扫描激光位姿敏感器获得的原始点云数据为输入，验证了算法在实际空间目标位姿测量中的性能。试验结果表明，该算法实现了原始点云93.1%的降采样，节省了92.9%的位姿解算时间，可有效提升星上数据处理的效率和姿态解算的实时性。

3D point cloud filtering method for pose measurement application of space non-cooperative targets

A point cloud feature extraction and filtering method for position and attitude(P & A) sensor of space non-cooperative target was presented, in order to filter the noise in raw point cloud obtained form laser P & A sensor and solve the problem that too many points taken part in the position and attitude computing wasted too much time. Then, using simulation method, the effectiveness of filtering the space rand noise and down-sample of point cloud was verified, and the robustness for target pose and Gauss measurement noise was tested. Finally, with the help of the all physical test platform for non-cooperative targets fly around, approach and capture, using the raw point cloud obtained from laser P & A sensor, the performance of the method in real position and attitude measurement was presented. The test results show that the algorithm achieves 93.1% down sampling of the original point cloud, saves 92.9% of the pose calculation time, which can effectively improve the efficiency of on-orbit data processing and the real-time performance of pose calculation.