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

作者：Shubin Si, Yulong Huang, Yiming Nie, Liang Xiao, Bin Dai, Yonggang Zhang · 发表于：IEEE Transactions on Aerospace and Electronic Systems · 年份：2024 · DOI：10.1109/taes.2024.3421173 · 被引用次数：9 · 研究领域：Robotics and Sensor-Based Localization、Remote Sensing and LiDAR Applications、Robotic Path Planning Algorithms

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. First, different from 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 spatiotemporal data association and the generative road surface models of covisibility regions. Finally, the proposed regionwise adaptive ROSE-constraint is integrated into the typical SLAM framework and tested on various custom and public datasets covering diverse terrain scenari...