PS-Loc: Robust LiDAR Localization with Prior Structural Reference

Prior structural reference like floor plan is readily accessible in indoor scene, which exhibits the potential of improving localization quality without the requirements of a previously-built high-precision map. This paper introduces a novel optimal transport-based framework for prior structural reference-based localization, aiming to improve the robustness for the robot localization. Leveraging the spacial relations of structures, a matching method based on optimal transport theory is proposed and it improves the robustness of matching results in dynamic scene and rapid rotation conditions. Additionally, this paper handles metric inaccuracies in the known structural reference by implementing an prior guided plane adjustment-based updating strategy. This strategy combines prior and observational information to jointly optimize the structural information within a sliding window. The performance of the framework is validated through real-world experiments, demonstrating superior accuracy and robustness to disturbances from dynamic occlusion and rapid rotation compared to common state-of-the-art SLAM and localization methods.