Ground surface detection in point cloud is widely used as a key module in autonomous driving systems. Different from previous approaches which are mostly developed for lidars with high beam resolution, e.g. Velodyne HDL-64, this paper proposes ground detection techniques applicable to much sparser point cloud captured by lidars with low beam resolution, e.g. Velodyne VLP-16. The approach is based on the RANSAC scheme of plane fitting. Inlier verification for plane hypotheses is enhanced by exploiting the point-wise tangent, which is a local feature available to compute regardless of the density of lidar beams. Ground surface which is not perfectly planar is fitted by multiple (specifically 4 in our implementation) disjoint plane regions. By assuming these plane regions to be rectanglar and exploiting the integral image technique, our approach approximately finds the optimal region partition and plane hypotheses under the RANSAC scheme with real-time computational complexity.
翻译:点云中的地面探测作为自主驱动系统的关键模块被广泛使用。与以前主要为高梁分辨率的激光雷达开发的方法不同,例如,Velodyne HDL-64,本文件提出地面探测技术适用于低梁分辨率的激光雷达捕捉到的更稀疏的云层,例如,Velodyne VLP-16。这种方法以RANSAC飞机装配计划为基础。通过利用点对亮的切线来增强对飞机假设的外部核查,这种切线是可用于计算不论激光束密度如何的局部特征。地面不是完全平整的,而是由多个(我们实施时具体为4个)不相连的平面区域安装的。假设这些平面区域是直角,并且利用综合图像技术,我们的方法可能发现RANSAC计划下的最佳区域分区分区分区和飞机假设物,并具有实时计算复杂性。