3D-BBS: Global Localization for 3D Point Cloud Scan Matching Using Branch-and-Bound Algorithm

TL;DR

The paper introduces 3D-BBS, a fast and accurate global localization method for 3D point clouds that leverages an extended branch-and-bound algorithm optimized with GPU processing and efficient data structures.

Contribution

It extends 2D BBS to 3D, introduces a sparse hash map, and a batched GPU-accelerated BnB algorithm for improved speed and accuracy in 3D global localization.

Findings

Achieves global localization in approximately 878 ms.

Outperforms state-of-the-art methods in accuracy.

Effective with scans aligned roughly in gravity direction.

Abstract

This paper presents an accurate and fast 3D global localization method, 3D-BBS, that extends the existing branch-and-bound (BnB)-based 2D scan matching (BBS) algorithm. To reduce memory consumption, we utilize a sparse hash table for storing hierarchical 3D voxel maps. To improve the processing cost of BBS in 3D space, we propose an efficient roto-translational space branching. Furthermore, we devise a batched BnB algorithm to fully leverage GPU parallel processing. Through experiments in simulated and real environments, we demonstrated that the 3D-BBS enabled accurate global localization with only a 3D LiDAR scan roughly aligned in the gravity direction and a 3D pre-built map. This method required only 878 msec on average to perform global localization and outperformed state-of-the-art global registration methods in terms of accuracy and processing speed.