基于激光点云的道路可通行区域检测方法

doi:10.19678/j.issn.1000-3428.0061717

摘要/Abstract

摘要： 以车载激光雷达获取的点云数据为研究对象，针对无人车道路环境感知的关键技术展开研究。为解决无人驾驶中道路可通行区域检测存在的地面不平整、缓坡、障碍物单一等问题，提出基于激光点云数据的道路可通行区域检测方法。通过基于分段校准的RANSAC算法进行地面分割，解决地面不平整导致的欠分割问题。使用多特征复合判据，利用基于体素化的DBSCAN聚类算法和基于结构特征的障碍物识别方法完成障碍物的分割与识别。结合道路结构以及数据高程突变特征，提取道路边界候选点并拟合得到完整的道路边界线。将道路区域栅格化，根据道路边界悬空障碍物判断并更新可通行区域，实现可通行区域的准确检测。实验结果表明，该方法在复杂道路场景中的边界检测准确率高于95%，可有效检测出障碍物及道路的可通行区域，具有良好的实时性与鲁棒性。

关键词: 无人驾驶, 分段校准, 障碍物分割与识别, 道路边界提取, 可通行区域

Abstract: This study focuses on several key technologies for unmanned vehicle road environment perception using the point cloud data obtained by vehicle lidar as the basis for the research.To solve the problems of uneven ground, gentle slope, and single obstacle when detecting passable areas in unmanned driving, this paper proposes a detection method for passable road areas based on laser point cloud data.The ground is segmented using the improved RANSAC algorithm based on sectional calibration to solve the problem of under-segmentation caused by uneven ground.Then, the multi-feature composite criterion is adopted to segment the obstacles.Obstacle recognition is performed by applying the DBSCAN clustering algorithm based on voxelization to structural features.Further, road boundary candidate points are extracted, and the road boundary line is fitted by combining the road structure and height mutation features.Finally, the passable area is updated by rasterizing the road area, combined with the judgment of suspended obstacles on the road boundary.The passable area can thus be accurately detected.The experimental results show that the boundary detection accuracy of this method in complex road scenes exceeds 95%, effectively detecting obstacles and passable areas of roads and displays good real-time performance and robustness.

Key words: unmanned driving, sectional calibration, obstacle segmentation and recognition, road boundary extraction, passable area

中图分类号:

TP391

宁小娟, 巩亮, 张金磊. 基于激光点云的道路可通行区域检测方法[J]. 计算机工程, 2022, 48(4): 22-29.

NING Xiaojuan, GONG Liang, ZHANG Jinlei. Detection Method of Passable Road Areas Based on Laser Point Clouds[J]. Computer Engineering, 2022, 48(4): 22-29.

http://www.ecice06.com/CN/Y2022/V48/I4/22

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