DOI: 10.11908/j.issn.0253-374x.20464

作者:

作者单位: 1.同济大学 汽车学院，上海 201804;2.同济大学 中德学部，上海 201804


作者简介: 周 苏（1961—），男，教授，博士生导师，工学博士，主要研究方向为新型车辆动力系统、燃料电池系统建模仿真及控制。E-mail： suzhou@tongji.edu.cn


通讯作者: 李伟嘉（1993—），男，硕士生，主要研究方向为自动驾驶高精度定位技术、高精度地图以及车用SLAM技术。E-mail： lwj19931221@163.com

中图分类号: U469.79


基金项目:




Lidar-based Localization Algorithm of Vehicle in Parking Lot
Author:

Affiliation: 1.School of Automotive Studies， Tongji University， Shanghai 201804， China;2.Chinesisch-Deutsche Hochschule， Tongji University， Shanghai 201804， China


Fund Project:




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摘要:在停车场、隧道中GPS、Wi-Fi信号受限的情况下，提出一种基于激光雷达的车辆自主定位方法。采用激光雷达SLAM（simultaneous localization and mapping）算法，通过三维激光雷达点云匹配获取车辆的估计位姿；根据图优化方法和非线性优化方法，对所有位姿进行后端调整，进而得到分辨率可控的环境信息平面栅格地图；基于蒙特卡洛方法，采用粒子滤波器进行实时车辆定位，并提出了粒子采样的一种改善方式，实现了较高精度的激光雷达自主定位。结果表明：粒子滤波器能够有效地实现车辆在停车场等无GPS环境下的定位，定位精度在10 cm之内。



Abstract:Under the limit of car localization sensors such as GPS and Wi-Fi in parking lots and tunnels， an autonomous self-localization method of vehicle based on lidar is proposed. The lidar simultaneous localization and mapping （SLAM） algorithm is used to obtain the estimated pose of vehicle through three-dimensional lidar point cloud matching， and all poses are adjusted according to the graph optimization method and the nonlinear optimization method. Then， a planar grid map of environmental information with controllable resolution is obtained. Based on the Monte Carlo method， a particle filter is adopted for real-time vehicle localization， and an improved method of particle sampling is proposed to achieve real-time high-precision autonomous localization of vehicle. Experimental results show that the particle filter can effectively realize the localization of vehicles in parking lot and other non-GPS environments， and the localization accuracy is within 10 cm.





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