by Huaxi (Yulin) Zhang 1, Yuyang Wang2, Xiaochuan Luo*, 3 
, Baptiste Mereaux4, Lei Zhang5
1 LTI, Université de Picardie Jules Verne, Saint Quentin, 02100, France
2 Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai, 201210, China
3 College of Information Science and Engineering,Northeastern University, Shenyang, 110819, China
4 Independent Researcher, Saint Quentin, 02100, France
5 euroDAO S.A.S., Saint Quentin, 02100, France
* Author to whom correspondence should be addressed.
Journal of Engineering Research and Sciences, Volume 3, Issue 2, Page # 22-42, 2024; DOI: 10.55708/js0302004
Keywords: Branch-and-bound, Global Localization, Position tracking, Robot kidnapping
Received: 04 January 2024, Revised: 09 February 2024, Accepted: 14 February 2024, Published Online: 29 February 2024
APA Style
Zhang, H., Wang, Y., Luo, X., Mereaux, B., & Zhang, L. (2024). Robust Localization Algorithm for Indoor Robots Based on the Branch-and-Bound Strategy. Journal of Engineering Research and Sciences, 3(2), 22-42. https://dx.doi.org/10.55708/js0302004
Chicago/Turabian Style
Zhang, Huaxi, Yuyang Wang, Xiaochuan Luo, Baptiste Mereaux, and Lei Zhang. “Robust Localization Algorithm for Indoor Robots Based on the Branch-and-Bound Strategy.” Journal of Engineering Research and Sciences 3, no. 2 (2024): 22-42. https://dx.doi.org/10.55708/js0302004.
IEEE Style
H. Zhang, Y. Wang, X. Luo, B. Mereaux, and L. Zhang, “Robust Localization Algorithm for Indoor Robots Based on the Branch-and-Bound Strategy,” Journal of Engineering Research and Sciences, vol. 3, no. 2, pp. 22-42, 2024. https://dx.doi.org/10.55708/js0302004.
Robust and accurate localization is crucial for mobile robot navigation in complex indoor environments. This paper introduces a robust and integrated robot localization algorithm designed for such environments. The proposed algorithm, named Branch-and-Bound for Robust Localization (BB-RL), introduces an innovative approach that seamlessly integrates global localization, position tracking, and resolution of the kidnapped robot problem into a single, comprehensive framework. The process of global localization in BB-RL involves a two-stage matching approach, moving from a broad to a more detailed analysis. This method combines a branch-and-bound algorithm with an iterative nearest point algorithm, allowing for an accurate initial estimation of the robot’s position. For ongoing position tracking, BB-RL uses a local map-based scan matching technique. To address inaccuracies that accumulate over time in the local maps, the algorithm creates a pose graph which helps in loop-closure optimization. Additionally, to make loop-closure detection less computationally intensive, the branch-and-bound algorithm is used to speed up finding loop constraints. A key feature of BB-RL is its Finite State Machine (FSM)-based relocalization judgment method, which is designed to quickly identify and resolve the kidnapped robot problem. This enhances the reliability of the localization process. BB-RL’s performance was thoroughly tested in real-world situations using commercially available logistics robots. These tests showed that BB-RL is fast, accurate, and robust, making it a practical solution for indoor robot localization.
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