基于激光雷达的道路不平度及可行驶区域检测

为了提高室外场景中车载激光雷达道路不平度信息检测的精度, 采用随机降采样和局部特征聚合的网络结构对道路环境信息进行提取分割。在分割过程中加入随机降采样的方法, 从而提高点云信息的计算效率, 为解决道路环境信息分割过程中关键特征丢失的问题, 加入局部特征聚合器来增加每个3维点云的接受域来保留几何细节。结果表明, 所提出的算法可以准确识别道路环境信息, 对于凸包、凹坑、道路可行驶区域的识别精度分别达到71.87%, 82.71%, 93.01%, 与传统卷积神经网络相比有显著提升。该研究可高效提取道路不平度及道路可行驶区域信息, 从而提高了车辆的主动安全性与平顺性。

Detection of road roughness and drivable area based on LiDAR

In order to improve the accuracy of road unevenness detection by vehicle-mounted lidar in outdoor scenes, the road environment information was extracted and segmented by the network structure of random down-sampling and local feature aggregation. Random sampling method was added in the segmentation process to improve the computing efficiency of high point cloud information. To solve the problem of the loss of key features in the segmentation process of road environment information, local feature aggregator was added to increase the acceptance domain of each 3-D point cloud to retain geometric details. The results show that the proposed algorithm can accurately identify the road environment information, and the recognition accuracy of convex hull, pit, and road able area reaches 71.87%, 82.71%, and 93.01% respectively, which is significantly improved compared with the traditional convolution neural network. This study can efficiently extract the information of road roughness and road able area. Thus, the active safety and ride comfort of the vehicle are improved.