针对无人船(USV)海上近距离实时性避碰检测的需求,提出一种基于三维激光雷达的USV障碍物自适应栅格表达方法。根据USV周边环境障碍物的激光雷达点云分布,建立障碍物密集度和障碍物表达时间与栅格地图分辨率之间的函数关系,自适应确定适中的地图分辨率,构建栅格地图;对三维激光雷达点云数据进行降维处理,将三维激光雷达点云投影到栅格地图,减小数据量,提高障碍物检测效率。利用三维激光雷达开展方法验证性实验,获取了三种不同障碍物场景的激光雷达点云数据。处理结果显示:环境中障碍物数量越多,获得的期望栅格地图分辨率越高,障碍物表达更精细;反之,障碍物数量越少,获得的期望栅格地图分辨率越低,障碍物表达更快速,可实现障碍物自适应栅格表达。所建立的方法可为后续USV局部避碰路径规划研究提供支撑。

To meet the demand for the real-time collision-avoidance detection of close obstacles by an unmanned surface vehicle (USV) on the sea surface, this study establishes an obstacle adaptive grid representation method for the USV based on three-dimensional (3D) lidar. According to the distribution of the lidar point cloud of environmental obstacles around the USV, a functional relationship among obstacle density, obstacle expression time, and grid map resolution is established for adaptively determining the moderate map resolution and constructing a grid map. The 3D lidar point cloud data are subjected to the process of dimensionality reduction and projected onto the grid map to reduce the amount of data and improve obstacle detection efficiency. Furthermore, a method validation experiment is conducted using 3D lidar; consequently, the lidar point cloud data of three different obstacle scenarios are obtained. The results show that the desired resolution of the obtained grid map and number of details regarding the obstacle increase with an increasing number of obstacles. Conversely, the desired resolution of the obtained grid map is lower and obstacle representation is faster with fewer obstacles in the environment, and the obstacle adaptive grid representation can be realized. The follow-up research of USV local collision avoidance path-planning can be supported by the established obstacle adaptive grid representation method.