PHMRNet: Persistent Homology Based Mamba-RWKV Network for LiDAR Place Recognition

Abstract

LiDAR-based place recognition (LPR) is a key component of visual localization and autonomous driving. Although LiDAR data are usually preprocessed by motion undistortion, which can greatly reduce scene distortion caused by sensor motion, 3-dimensional (3D) point clouds in complex scenes still show inherent unstructured sparsity. Projecting 3D point clouds into 2D range view (RV) images can provide structured feature representations that are more suitable for neural networks and deep learning methods. However, this strategy has two main limitations: 1) the important geometric details are removed by the projection, impairing the effective extraction and representation of scene features. 2) unpredictable occlusions and viewpoint changes alter the observed scene structure and often lead to incorrect place matches. To address these limitations, we propose a LPR network that combines topological information extracted by persistent homology (PH) with RV features to compensate for missing structural information and produce more stable descriptors. Specifically, PH-stable features are added as a topology-aware sampling channel for the RV feature to integrate more stable and discriminative robust geometric cues. Next, we introduce a spatiotemporal fusion module that links topological and RV features across consecutive frames, thereby improving spatiotemporal consistency of the representation and thus reducing the impact of occlusions and rotations. Compared with OverlapMamba, PHMRNet can better handle spatial, temporal, and geometric features, improving the robustness of the descriptor. Experiments on public datasets show that PHMRNet outperforms existing methods in terms of accuracy and robustness under the same driving conditions and LiDAR scanning rate.