摘要
传统基于点云分析的物体与场景干涉检测算法使用层次包围盒或空间分解的方法判断是否发生干涉,不能获取物体与场景各点的准确安全距离数值。提出一种基于双目视觉点云的非凸复杂形貌物体干涉分析方法,该方法首先使用双目立体视觉算法对已标定的双目相机拍摄的场景快速重建点云,然后用待分析的物体三维点云和重建的场景点云数据求解干涉问题,使用K-D树搜索的方法快速确定指定点的干涉距离,使用相机光轴方向的点云坐标关系确定是否干涉。方法在某探测器内场实验中的干涉检测正确率为100%。且相较于现有干涉检测算法,本方法可准确获取物体表面是否干涉及具体距离信息,并借助双目点云沿参考相机光轴方向有效简化相交测试计算复杂度,在降采样下单点检测的时间不超过0.15 s,能够满足非凸复杂形貌物体和各类地形的干涉快速分析的需求。所提方法圆满完成了嫦娥五号在轨月面采样封装中采样点选择的采样器-地形干涉分析任务。
The traditional collision detection algorithm based on the point cloud typically uses bounding volume hierarchies or space decomposition to determine whether there is collision. This method cannot achieve the accurate safety distance value between the object and the scene. In this study, a collision analysis method based on the binocular stereo point cloud is proposed, which is mainly for non-convex complex surface objects. Firstly, the binocular stereo algorithm is used to reconstruct the point cloud of the scene captured by the calibrated camera. Then, the point clouds of the object and the scene are both utilized to solve the collision problem. The process distance values are rapidly obtained by the K-D tree search algorithm. The symbol is defined by the coordinate relationship of the point cloud along the optical axis of the camera. The accuracy of this method in the field experiment of a detector is 100%. Compared with the existing algorithms, this method can obtain the distance of each point on the surface of the object. The complexity of calculation is reduced efficiently under the reference of camera′s optical axis. The detection time at the drop sampling single point is not larger than 0.15 s, which can satisfy the need of rapid collision analysis between non-convex objects and complex topography. This method can successfully complete the sampler-terrain collision analysis task of sampling point selection in the lunar surface sampling package of Chang′e-5.
作者
张湛舸
王乾一
屈玉福
Zhang Zhange;Wang Qianyi;Qu Yufu(School of Instrumentation and Optoelectronic Engineering,Beihang University,Beijing 100191,China;Hangzhou Hikvision Digital Technology Co.,Ltd.,Hangzhou 310051,China)
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2021年第3期262-269,共8页
Chinese Journal of Scientific Instrument
基金
国家自然科学基金(51675033)项目资助
关键词
双目重建
干涉分析
K-D树
stereo reconstruction
collision analysis
K-D tree
