基于RANSAC的公路路面坑槽提取方法
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陕西省交通运输厅科技项目(20-25X)


Highway Road Pothole Extraction Method Based on RANSAC
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    摘要:

    针对基于三维激光点云的坑槽扫描提取算法计算量大、效率低的问题, 提出基于RANSAC思想的坑槽提取方法. 首先, 使用RANSAC计算横断面基准线, 矫正横断面数据并初步识别坑槽点及其位置; 其次, 对坑槽区域使用RANSAC计算坑槽局部基准路面, 由此标记出坑槽点及路面点; 然后使用种子填充算法进行连通域求解, 计算出坑槽点集; 最后对坑槽点集进行坑槽边界提取及坑槽数据分析. 实验结果表明, 使用RANSAC算法能够快速扫描横断面点云数据, 相对于使用曲率特征点检测算法, 其处理时间平均提升56.46%; 并且对提取坑槽的深度和面积具有良好的效果, 准确度高, 深度平均误差为4.73%、面积平均误差为4.50%.

    Abstract:

    To tackle the problems of heavy calculation burden and low efficiency of the pothole extraction algorithm based on the scanning of three-dimensional (3D) laser point clouds, this study proposes a pothole extraction method based on RANSAC. Firstly, RANSAC is employed to calculate the cross-sectional baseline for the correction of cross-sectional data and preliminary identification of pothole points and their locations. Secondly, the local reference road surface near the pothole is calculated by RANSAC so that the pothole points and road surface points can be marked. Thirdly, the seed filling algorithm is used to solve the connected domain and calculate the set of pothole points. Finally, the edge of the pothole is extracted with the set of pothole points and an exhaustive analysis of the pothole data is made. The experimental results show that RANSAC can quickly scan cross-sectional point cloud data, with the processing time increased by 56.46% on average compared with that of the curvature feature point detection algorithm. It has a good effect on extracting the depth and area of potholes with high accuracy. The average error of depth and area is 4.73% and 4.50%, respectively.

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廖飞钦,马荣贵,王朵,陈鑫龙.基于RANSAC的公路路面坑槽提取方法.计算机系统应用,2022,31(5):230-237

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  • 收稿日期:2021-07-20
  • 最后修改日期:2021-08-18
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  • 在线发布日期: 2022-04-11
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