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 with the existing pose estimation methods. In addition, the demand for real-time performance further increases the difficulty of the pose estimation task. In this paper, we propose a novel vehicle pose estimation method based on the convex hull. The extracted 3D cluster is reduced to the convex hull, reducing the subsequent computation burden while preserving essential contour information. Subsequently, a novel criterion based on the minimum occlusion area is developed for the search-based algorithm, enabling accurate pose estimation. Additionally, this criterion renders the proposed algorithm particularly well-suited for obstacle avoidance. The proposed algorithm is validated on the KITTI dataset and a manually labeled dataset acquired at an industrial park. The results demonstrate that our proposed method can achieve better accuracy than the classical pose estimation method while maintaining real-time speed.

翻译：激光雷达车辆位姿估计是自动驾驶感知技术中的核心问题。然而，由于激光雷达点云的观测不完整性和稀疏性，现有基于三维激光雷达的位姿估计方法难以获得令人满意的结果。此外，实时性要求进一步增加了位姿估计任务的难度。本文提出一种基于凸包的新型车辆位姿估计方法：将提取的三维聚类简化为凸包，在保留关键轮廓信息的同时降低后续计算负担；进而针对基于搜索的算法提出最小遮挡区域判据，实现精确位姿估计。该判据使算法特别适用于避障场景。通过在KITTI数据集及工业园区手工标注数据集上的验证，结果表明，本方法在保持实时性前提下，相较于经典位姿估计方法具有更高的精度。