MRMT-PR: A Multi-Scale Reverse-View Mamba-Transformer for LiDAR Place Recognition

Abstract

Place recognition is a fundamental technology of high relevance for autonomous robot navigation. Existing methods encounter significant challenges arising from scene variations (e.g., illumination changes, dynamic objects), view-point shifts, and difficulties in data fusion and alignment. These factors often lead to a substantial drop in recognition recall, which is typically addressed in the literature by training deep neural networks to learn invariant feature representations. In this paper, we propose MRMT-PR, a novel multi-scale reverse-view Mamba-Transformer architecture for LiDAR-based place recognition that uses a single-frame point cloud as its input. Our MRMT-PR framework consists of a multi-scale reverse-view preprocessing module for LiDAR point clouds, a Mamba-Transformer feature encoder, and a global feature fusion module. This architecture effectively mitigates the impact of perspective and illumination variations, enhances the global representational capacity of LiDAR features, and significantly improves recognition robustness under challenging conditions such as viewpoint changes and long-term localization. Experiments conducted on NCLT dataset with challenging scenarios demonstrate that MRMT-PR outperforms existing LiDAR-based place recognition baselines in terms of overall performance.