基于ICP算法的非合作目标特征点云配准优化

Non-cooperative Target Feature Point Cloud Registration Optimization Based on ICP Algorithm

针对视觉测量中非合作目标无法提供合作信息从而引起的位姿测量问题,采用ICP(Iterative Closest Point)算法配准不同时刻获取的点云降采样数据来完成目标的相对位姿测量。利用运动恢复结构算法获取了目标当前时刻的点云数据并比较了基于阈值匹配与光流匹配的特征点匹配算法,利用三角测量法对提取到的特征点进行重建,同时将点云数据进行降采样处理,根据降采样后的点云数据计算出不同时刻目标的相对位姿关系。实验表明当物体发生旋转运动时,对降采样数据利用ICP算法计算得到的目标绕坐标轴旋转角度最大误差不超过0.11°。

Aiming at the pose measurement caused by non-cooperative targets in visual measurement that cannot provide cooperation information, the ICP(Iterative Closest Point) algorithm is used to register the point cloud down-sampling data acquired at different times to complete the relative pose measurement of the target. The point cloud data of the target at the current moment is obtained using the structure from motion algorithm and the feature point matching algorithms are compared based on threshold matching and optical flow matching method. The extracted feature points are reconstructed by triangulation. The relative pose changes of the object at different times are calculated by using the downsampling point cloud data. Experiments show that when the object rotates, the maximum error of the rotation angle using the ICP algorithm does not exceed 0.11°.