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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 tablefor storing hierarchical 3D voxel maps. To improve the processing cost of BBSin 3D space, we propose an efficient roto-translational space branching andbest-first search strategy. Furthermore, we devise a batched BnB algorithm tofully leverage GPU parallel processing. Through experiments in simulated andreal environments, we demonstrated that the 3D-BBS enabled accurate globallocalization with only a 3D LiDAR scan and a 3D pre-built map. This methodrequired only 878 msec on average to perform global localization andoutperformed state-of-the-art feature-matching-based global localizationmethods in terms of accuracy and processing speed.

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