An Efficient Convex Hull-based Vehicle Pose Estimation Method for 3D LiDAR

TL;DR

This paper introduces a convex hull-based vehicle pose estimation method for 3D LiDAR data that improves accuracy and real-time performance, addressing challenges of sparse and incomplete point clouds in autonomous driving.

Contribution

It presents a novel convex hull reduction technique and a minimum occlusion area criterion for efficient and accurate vehicle pose estimation from LiDAR data.

Findings

Achieves better accuracy than classical methods.

Maintains real-time speed in pose estimation.

Validated on KITTI and industrial datasets.

Abstract

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.