Place recognition is the foundation for enabling autonomous systems to achieve independent decision-making and safe operations. It is also crucial in tasks such as loop closure detection and global localization within SLAM. Previous methods utilize mundane point cloud representations as input and deep learning-based LiDAR-based Place Recognition (LPR) approaches employing different point cloud image inputs with convolutional neural networks (CNNs) or transformer architectures. However, the recently proposed Mamba deep learning model, combined with state space models (SSMs), holds great potential for long sequence modeling. Therefore, we developed OverlapMamba, a novel network for place recognition, which represents input range views (RVs) as sequences. In a novel way, we employ a stochastic reconstruction approach to build shift state space models, compressing the visual representation. Evaluated on three different public datasets, our method effectively detects loop closures, showing robustness even when traversing previously visited locations from different directions. Relying on raw range view inputs, it outperforms typical LiDAR and multi-view combination methods in time complexity and speed, indicating strong place recognition capabilities and real-time efficiency.

翻译：位置识别是自主系统实现独立决策和安全运行的基础，同时在SLAM中的闭环检测和全局定位等任务中至关重要。以往方法采用常规点云表征作为输入，并基于深度学习的激光雷达位置识别（LPR）方法，通过卷积神经网络（CNN）或Transformer架构处理不同点云图像输入。然而近期提出的Mamba深度学习模型，结合状态空间模型（SSM），在长序列建模方面展现出巨大潜力。为此，我们开发了OverlapMamba这一新型位置识别网络，将输入的距离视图（RVs）作为序列处理。该方法创新性地采用随机重构策略构建移位状态空间模型，压缩视觉表征。在三个不同公开数据集上的评估表明，我们的方法能有效检测闭环，即使在从不同方向穿越已访问区域时仍展现出鲁棒性。基于原始距离视图输入，该方法在时间复杂度和速度上优于典型的激光雷达及多视图组合方法，展现出强大的位置识别能力与实时处理效率。