基于网格单元的点云降维处理算法

针对激光雷达采集数据时，由于会受到外界的干扰因素、扫描精度等负面影响，会使采集到的点云数据空间密度相差较大、存在着很大的噪声和孔洞，使得分析结果不能直接描绘实际物体的模型的问题，本文研究设计了一种基于二进制占网格的点云数据处理算法。首先将分割完成后的点云采用二进制网格的方式聚类进行降维处理，再将点云映射到网格单元中实现不同物体点云的快速聚集。最后，基于寻找出的点云主方向旋转点云从而.建立紧致随动的障碍物包围盒。通过实验验证，该方法能够在保证聚类精度的同时提高运算速度，其建立包围盒能够准确地反映障碍物的尺寸，具有良好的实时性与随动性，对移动机械臂自主避障提供了可靠的信息。

Dimension reduction algorithm of point cloud based on grid cell

In view of the problem that when lidar collects data, due to the negative impact of external interference factors and scanning accuracy, the spatial density of the collected point cloud data will vary greatly, and there will be a lot of noise and holes, so that the analysis results can not directly describe the model of the actual object, this paper designs a point cloud data processing algorithm based on binary grid occupation. First, the point cloud after segmentation is clustered by binary grid to reduce the dimension, and then the point cloud is mapped to the grid cell to achieve rapid aggregation of different object point clouds. Finally, the point cloud is rotated based on the main direction found Establish a closely followed obstacle bounding box. The experimental results show that this method can improve the operation speed while ensuring the clustering accuracy. Its bounding box can accurately reflect the size of obstacles, and has good real-time and follow-up performance. It provides reliable information for the mobile robot to avoid obstacles autonomously.