OverlapMamba: A Shift State Space Model for LiDAR-Based Place Recognition

Abstract

Place recognition is the foundation for autonomous systems to achieve independent decision-making and secure operation. It is also crucial in tasks such as loop closure detection and global localization in Simultaneous Localization and Mapping (SLAM) technology. Existing LiDAR-based place recognition (LPR) methods use raw point cloud representations or multifarious point cloud representations as inputs, as well as employ convolutional neural networks or transformer architectures. However, the recently proposed Mamba deep learning model combined with State Space Models (SSMs) has enormous potential in long sequence modeling. Therefore, we have developed a novel place recognition network OverlapMamba, which represents input range views (RVs) as sequences. In a novel way, we use a stochastic reconstruction method to establish shifted state space models to compress the visual representation. Extensive experiments on three public datasets demonstrate that OverlapMamba achieves competitive performance with real-time inference speed, which effectively detects loop closure even when traversing previously visited locations from different directions, indicating its strong place recognition ability and real-time efficiency.