为解决自动驾驶系统中车辆自主定位与导航无法准确估计车身位姿及导航路径不够平滑等问题，提出一种基于先验激光雷达点云地图的定位与导航方法。利用点云分割技术分离出可行区域以及潜在的风险源，研究基于优化收敛流程的NDT(Normal Distribution Transform)点云配准定位方法，并对传统A*算法从动态权重设计和扩展领域优先搜索策略两方面进行改进，以适应自动驾驶的实时定位与导航需要。实验采用百度Apollo自动驾驶开发套件(D-KIT)进行多组对照实验，在体素降采样Leafsize参数为1(高采样)、1。2(中采样)与1。5(低采样)时，定位耗时分别降低了27。77%，38。75%和 38。30%。选取四组符合实际驾驶需求情况进行导航实验，改进后导航路径最大曲率分别降低了 80。9%，74。9%，65%，69。5%，导航过程路径曲率保持较低且稳定平滑，曲率数据符合车辆动力学。为车辆定位与高精度导航提供有效方法。
Vehicle localization and navigation method based on LiDAR point cloud map
In order to solve the problems of vehicle autonomous positioning and navigation in the auto drive system,such as the inability to accurately estimate the body posture and the unsmooth navigation path,a positioning and navigation method based on a priori laser radar point cloud map was proposed.Us-ing point cloud segmentation technology to separate feasible areas and potential risk sources,this paper studies the NDT(Normal Distribution Transform)point cloud registration and localization method based on optimized convergence process.The traditional A* algorithm is improved from two aspects:dynamic weight design and domain first search strategy to meet the real-time positioning and navigation needs of au-tonomous driving.The experiment used Baidu Apollo Autonomous Driving Development Kit(D-KIT)for multiple control experiments.When the voxel downsampling Leafsize parameter was 1(high sam-pling),1.2(medium sampling),and 1.5(low sampling),the localization time was reduced by 27.77%,38.75%,and 38.30%,respectively.Four sets of navigation experiments were selected that meet the ac-tual driving needs.After improvement,the maximum curvature of the navigation path was reduced by 80.9%,74.9%,65%,and 69.5%,respectively.The curvature of the navigation path remained low and stable,and the curvature data was consistent with vehicle dynamics.Provide effective methods for vehicle positioning and high-precision navigation.
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