面向智能汽车的地下停车场定位与路径规划

Location and path planning in underground parking lot for intelligent vehicles

为了解决智能车辆在全球定位系统(Global Positioning System,GPS)信号缺失的地下停车场中的定位与路径规划问题,本文使用双目离线建图、单目在线定位的视觉方法在场景中进行定位,并使用改进的路径规划算法规划全局行驶路线。首先,利用双目相机在地下停车场中的多个节点位置进行场景采集,节点的不同信息分层存储入视觉多层级地图。然后,利用单目相机进行由粗到精的地图匹配和位姿计算实现快速高精度定位。最后,使用改进的路径规划算法在地图中对各路口位置进行检测并生成路口路径图(Intersection Roadmap,IRM),全局可行驶路线根据定位结果和路径图规划得到。实验结果表明:使用单目相机的视觉定位平均误差为1.3 m,平均误差率为7.4%,基于路口路径图的规划算法相比概率路径图(Probabilistic Roadmap,PRM)算法,规划时间由5 s左右缩短至0.2 s以内,路径长度缩短了约9.56%。定位导航系统能够实时准确地实现车辆在停车场环境中的定位与路径规划,具备良好的实用性。

To solve the location and path planning challenges for intelligent vehicles in GPS-restricted underground parking lots,we employ a vision-based approach that combines binocular offline mapping and monocular online localization to trace the intelligent vehicles within the scene.The improved path planning algorithm is used to plan the global route.First,the system uses a binocular camera to capture scenes at multiple nodes in the underground parking lot,which are then stored in different layers of a hierarchical map.The monocular camera is subsequently utilized to perform coarse-to-fine map matching and calculate the pose of the vehicle,ensuring high-speed and accurate positioning.Finally,the improved path planning algorithm is achieved by detecting intersections and generating a roadmap.The experimental results demonstrate that the average monocular positioning error and average error rate are 1.3 m and 7.4%,respectively,the planning time is reduced from approximately 5 s to less than 0.2 s,and the path length is shortened by approximately 9.56%.This positioning and navigation system is practical for real-time positioning and path planning of intelligent vehicles in parking lot environments.