基于改进RANSAC算法的非结构化道路坡度识别

Liang Hong, Lijin Han, Hui Liu
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引用次数: 0

摘要

非结构化的道路场景通常具有颠簸起伏的特征,并且地面上经常有坑洼和石头等障碍物。同时,低光束激光雷达点云相对稀疏,容易影响坡度检测结果的准确性。针对这些问题,本文提出了一种基于改进RANSAC算法的非结构化道路坡度实时检测方法。利用惯性导航获取车辆运动信息,融合历史帧点云丰富点云密度,然后对点云进行多尺度栅格化处理,再利用改进的RANSAC算法对点云进行拟合,最后得到道路坡度。实验结果表明,该算法提高了检测精度、实时性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unstructured Road Slope Recognition Based on Improved RANSAC Algorithm
Unstructured road scenes usually have bumpy and undulating features, and there are often obstacles such as potholes and stones on the ground. At the same time, the lidar point cloud with low beams is relatively sparse, which easily affects the accuracy of slope detection results. In response to these problems, this paper proposes a real-time detection method for unstructured road slope based on the improved RANSAC algorithm. Use inertial navigation to obtain vehicle motion information, fuse historical frame point clouds to enrich the point cloud density, then perform multi-scale rasterization on the point cloud, and then use the improved RANSAC algorithm to fit the point cloud, and finally get the slope of the road. Experimental results verify that the algorithm can improve detection accuracy, real-time performance, and effectiveness.
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