Abstract
Robust and accurate localization is crucial for mobile robot navigation in complex indoorenvironments. This paper introduces a robust and integrated robot localization algorithm designedfor such environments. The proposed algorithm, named Branch-and-Bound for Robust Localization(BB-RL), introduces an innovative approach that seamlessly integrates global localization, positiontracking, and resolution of the kidnapped robot problem into a single, comprehensive framework. Theprocess of global localization in BB-RL involves a two-stage matching approach, moving from a broadto a more detailed analysis. This method combines a branch-and-bound algorithm with an iterativenearest point algorithm, allowing for an accurate initial estimation of the robot’s position. For ongoingposition tracking, BB-RL uses a local map-based scan matching technique. To address inaccuracies thataccumulate over time in the local maps, the algorithm creates a pose graph which helps in loop-closureoptimization. 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 FiniteState Machine (FSM)-based relocalization judgment method, which is designed to quickly identify andresolve the kidnapped robot problem. This enhances the reliability of the localization process. BB-RL’sperformance was thoroughly tested in real-world situations using commercially available logisticsrobots. These tests showed that BB-RL is fast, accurate, and robust, making it a practical solution forindoor robot localization.