ROSE: Covisibility Region Aware 3D-LiDAR SLAM Based on Generative Road Surface Model and Long-Term Association

Light detection and ranging (LiDAR)-based Simultaneous Localization and Mapping (SLAM), known for its precision and resilience to interference, has been widely utilized in open and unknown environments. However, the common LiDAR sensors (e.g., VLP-16) face many limitations such as a small vertical field of view, hindering the provision of adequate vertical motion constraints, which leads to easier error accumulations in the vertical direction and a significant hurdle for deployment in large-scale scenarios. Motivated by this problem, a covisibility region aware 3D-LiDAR SLAM method, is proposed based on generative road surface model and long-term association. Firstly, different to existing methods that often overlook the ground points, the role of ground points is reexamined from a fresh perspective, and the intrinsic relationship between the ground points and the sequential states is established, which specifically compensates for the inadequacy of vertical pose constraints. Then, a pipeline for processing ground points within generalized terrains is proposed, and a long-term pose constraint (denoted as ROSE-constraint) for region-level tracking of road surfaces is constructed based on the proposed spatio-temporal data association and the generative road surface models of covisibility regions. Finally, the proposed region-wise adaptive ROSE-constraint is integrated into the typical SLAM framework and tested on various custom and public datasets covering diverse terrain scenarios. Experimental results demonstrate the effectiveness and superiority of the proposed method compared to existing popular and state-of-the-art LiDAR-based SLAM solutions. Source codes will be available at https://github.com/SiShuBin/ROSE .