  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 is publicly available at
https://github.com/Terminal-K/MambaMOS.
