Environmental Perception of Multi-layer Laser Radar in a Driverless Car
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摘要: 为了使无人驾驶车获得可行驶区域和障碍物信息, 通过分析大量激光雷达扫描点数据, 总结并得出路沿数据点独有的特征, 提出一种基于路沿数据点特征和多层融合技术的路沿检测算法.应用Dezert-Smarandache理论 (Dezert-Smarandache theory, DSm T) 对无人驾驶车前方道路环境建立栅格地图, 并利用证据理论中的冲突系数检测动态障碍物.最后, 采用膨胀算法、侵蚀算法和改进的八邻域区域标记算法对动态障碍物进行聚类和信息提取.实车实验结果表明:本算法可稳定、准确地感知无人驾驶车周围环境信息.Abstract: To obtain the information of drivable area and obstacles for a driverless car, unique characteristics of the road edge data points were summarized and concluded by analyzing a large number of scanning lidar data, and a road edge detection algorithm was proposed based on the features of road edge data points and multi-layer fusion technology. DSm T was applied to establish a grid map for the road environment in front of the unmanned vehicle. The DSm T conflict coefficient was used to detect dynamic obstacles. Finally, the clustering and information extraction of dynamic obstacles was completed by the expansion algorithm, erosion algorithm, and the improved eight neighborhood labeling algorithm.Resultsshow that the algorithm can stably and accurately perceive the environment information around driverless vehicle.
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