现阶段车载点云道路提取的研究主要为有规则路坎的结构化道路, 而在现实中有些道路两侧是草地, 结构化道路的提取方法不再适用.针对此问题, 本文提出一种融合移动窗口高差和相邻点序号差的路面自动提取方法.首先利用相邻点间距实现扫描线的提取, 接着根据移动窗口高差和相邻点序号差提取道路边界点, 并采用RANSAC(RANdom SAmple Consensus, 随机抽样一致)算法对道路边界点进行拟合, 在此基础上, 根据线性规划原理和滤波处理提取路面点云.以两组道路数据进行实验分析, 路面提取的完整率分别为99.79%和99.52%, 正确率分别为99.91%和99.62%, 提取质量分别为99.70%和99.15%.实验结果表明, 该方法能够同时实现结构化道路路面和路边为草地的非结构化道路路面的有效提取.
At present, the research of road extraction using vehicle-borne point cloud mainly focuses on structured roads with regular road cuts.However, some roads in reality are flanked by grass, and the extraction method of structured roads is no longer applicable.To address this problem, an automatic road surface extraction method integrating the moving window height difference and neighboring point serial number difference is proposed.First, the adjacent point space is used to realize the extraction of scan lines, and the road boundary points are extracted according to the moving window height difference as well as the adjacent point serial number difference.Next, the road boundary points are fitted with RANSAC algorithm.Afterwards, the road surface point cloud is extracted according to the linear programming principle and filter processing.Finally, two sets of road data are used to test the proposed method.The completeness of road surface extraction is 99.79% and 99.52%, the correctness is 99.91% and 99.62%, and the quality is 99.70% and 99.15%, for the two datasets respectively.The experimental results show that the proposed method is applicable for road surface extraction of structured road and unstructured road with grass on the roadside.
MA Xirui, SHEN Yueqian, HUANG Teng. Automatic extraction of road surface point cloud considering the difference of neighboring point serial numbers[J]. Journal of Nanjing University of Information Science & Technology, 2021,13(6):661-668
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