基于凹包算法的点云地面分割方法

为了解决现有的地面分割方法在路面复杂、点云稀疏场景下存在的地面分割不准确的问题，提出一种基于凹包算法的地面分割算法。该方法首先根据激光雷达点云生成凹包，然后根据粗滤提取的三角面内点的分布和三角面法向量的扫描特性，将地面三角面选取出来，之后再精确提取地面三角面的内点，根据内点到三角面的距离即可精确的完成地面分割。实验结果表明，该方法可以充分考虑点云周围的几何特征，对物体的几何边界敏感，可以在地面倾斜的场景下精细的将小凸起、路牙石等小型障碍物分割出来。

Method of point cloud ground segmentation based on concave hull

In order to solve the problem that the existing ground segmentation methods are inaccurate in the complex road surface and sparse point cloud scene, proposes a ground segmentation algorithm based on concave bag algorithm. This method first generates concave packets according to the lidar point cloud, then selects the ground triangle according to the difference of the points in the triangle surface extracted by rough filtering and the scanning characteristics of the normal vector of the triangle surface, and then accurately extracts the interior points of the ground triangle surface. The ground segmentation can be completed accurately according to the distance from the interior point to the triangle surface. The experimental results show that this method can fully consider the geometric characteristics around the point cloud, and is sensitive to the geometric boundary of the object. It can finely segment small bumps, kerbstones and other small obstacles in the scene with sloping ground.