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

Koki Aoki, Kenji Koide, Shuji Oishi, Masashi Yokozuka, Atsuhiko Banno, Junichi Meguro

TL;DR

An accurate and fast 3D global localization method, 3D-BBS, that extends the existing branchand-bound (BnB)-based 2D scan matching (BBS) algorithm and proposes an efficient roto-translational space branching to improve the processing cost of BBS in 3D space.

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.

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

TL;DR

An accurate and fast 3D global localization method, 3D-BBS, that extends the existing branchand-bound (BnB)-based 2D scan matching (BBS) algorithm and proposes an efficient roto-translational space branching to improve the processing cost of BBS in 3D space.

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.
Paper Structure (14 sections, 12 equations, 7 figures, 3 tables, 2 algorithms)

This paper contains 14 sections, 12 equations, 7 figures, 3 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Global Localization with Point Cloud Matching
  4. Point Cloud Matching Using Branch-and-Bound Method
  5. Methodology
  6. Problem Setting
  7. 2D Branch-and-Bound Scan Matching
  8. Multi-resolution Voxel Map
  9. Branching Algorithm
  10. GPU-accelerated Batched Branch-and-Bound
  11. Experiments
  12. Simulated Environment
  13. Real Environment
  14. Conclusion

Figures (7)

  • Figure 1: Overview of the proposed global localization.
  • Figure 2: Overview of the branching operation in the proposed method. (a) Translational branching. (b) Roll and pitch angle branching. (c) Yaw angle branching.
  • Figure 3: Simulated environment.
  • Figure 4: 3D point cloud map in the experiment with the simulated environment. The map size is 211.0 $\times$ 729.7 $\times$ 68.5 $\rm{m^{3}}$. The total number of evaluation points represented by red dots is 295.
  • Figure 5: Processing time of the configurations (a) - (i) in the simulated environment.
  • ...and 2 more figures