Vehicle pose estimation with LiDAR is essential in the perception technology of autonomous driving. However, due to incomplete observation measurements and sparsity of the LiDAR point cloud, it is challenging to achieve satisfactory pose extraction based on 3D LiDAR by using the existing pose estimation methods. In addition, the requirement for real-time performance further increases the difficulty of the pose estimation task. In this paper, we proposed a novel convex hull-based vehicle pose estimation method. The extracted 3D cluster is reduced to the convex hull, reducing the computation burden and retaining contour information. Then a novel criterion based on the minimum occlusion area is developed for the search-based algorithm, which can achieve accurate pose estimation. This criterion also makes the proposed algorithm especially suitable for obstacle avoidance. The proposed algorithm is validated on the KITTI dataset and a manually labeled dataset acquired at an industrial park. The results show that our proposed method can achieve better accuracy than the state-of-the-art pose estimation method while maintaining real-time speed.

翻译：基于激光雷达的车辆位姿估计是自动驾驶感知技术中不可或缺的组成部分。然而，由于激光雷达点云存在观测不完整和稀疏性等问题，现有位姿估计方法难以基于三维激光雷达实现令人满意的位姿提取。此外，实时性要求进一步增加了位姿估计任务的难度。本文提出了一种新颖的基于凸包的车辆位姿估计方法。该方法将提取的三维聚类简化为凸包，在保留轮廓信息的同时降低了计算负担。随后针对基于搜索的算法开发了一种基于最小遮挡区域的新准则，能够实现精确的位姿估计。该准则还使所提算法特别适用于避障场景。所提算法在KITTI数据集和工业园区采集的人工标注数据集上进行了验证。结果表明，与现有最优位姿估计方法相比，我们的方法在保持实时处理速度的同时能够达到更高的精度。