LiDAR-based Indoor Localization with Optimal Particle Filters using Surface Normal Constraints

Accurate and robust localization systems are often highly desired in autonomous mobile robots. Existing LiDAR-based localization systems generally use standard particle filters which suffer from the well-known particle degeneracy problem. Furthermore, standard particle filters are ill-suited for handling discrepancies between maps and the actual operating environments. In this work, we present an effective LiDAR-based indoor localization system which addresses these two issues. The particle degeneracy problem is tackled with an efficient implementation of an optimal particle filter. Map discrepancies are then handled with the use of a high-fidelity observation model for accurate particle propagation and a separate low-fidelity observation model for robust weight update. Evaluations were carried out against a standard particle filter baseline on both real-world and simulated data from challenging indoor environments. The proposed system was found to show significantly better performance in-terms of accuracy, robustness to ambiguity, and robustness to map discrepancies. These performance gains were observed even with more than ten times smaller particle set sizes than in the baseline, while the increase in the computation time per particle was only around 20%.