LiDAR-based Moving Object Segmentation (MOS) aims to locate and segment moving objects in point clouds of the current scan using motion information from previous scans. Despite the promising results achieved by previous MOS methods, several key issues, such as the weak coupling of temporal and spatial information, still need further study. In this paper, we propose a novel LiDAR-based 3D Moving Object Segmentation with Motion-aware State Space Model, termed MambaMOS. Firstly, we develop a novel embedding module, the Time Clue Bootstrapping Embedding (TCBE), to enhance the coupling of temporal and spatial information in point clouds and alleviate the issue of overlooked temporal clues. Secondly, we introduce the Motion-aware State Space Model (MSSM) to endow the model with the capacity to understand the temporal correlations of the same object across different time steps. Specifically, MSSM emphasizes the motion states of the same object at different time steps through two distinct temporal modeling and correlation steps. We utilize an improved state space model to represent these motion differences, significantly modeling the motion states. Finally, extensive experiments on the SemanticKITTI-MOS and KITTI-Road benchmarks demonstrate that the proposed MambaMOS achieves state-of-the-art performance. The source code of this work will be made publicly available at https://github.com/Terminal-K/MambaMOS.

翻译：基于LiDAR的运动物体分割（MOS）旨在利用先前扫描的运动信息，在当前扫描点云中定位并分割运动物体。尽管现有MOS方法已取得令人鼓舞的结果，但时空信息耦合较弱等关键问题仍需进一步研究。本文提出一种新颖的基于LiDAR的三维运动物体分割方法——运动感知状态空间模型（MambaMOS）。首先，我们开发了一种新型嵌入模块——时间线索引导嵌入（TCBE），用于增强点云中时空信息的耦合，缓解时间线索被忽略的问题。其次，引入运动感知状态空间模型（MSSM），使模型能够理解同一物体在不同时间步中的时序相关性。具体而言，MSSM通过两个不同的时序建模与相关步骤，突出同一物体在不同时间步的运动状态。我们利用改进的状态空间模型表征这些运动差异，从而有效建模运动状态。最后，在SemanticKITTI-MOS和KITTI-Road基准上的大量实验表明，所提出的MambaMOS达到了最优性能。本工作的源代码将开源发布于https://github.com/Terminal-K/MambaMOS。