ATCM: Aerial–Terrestrial LiDAR-Based Collaborative Simultaneous Localization and Mapping

Multi-robot collaborative simultaneous localization and mapping (C-SLAM) offers precise scene reconstruction over single-robot SLAM and enables data fusion from heterogeneous robots. However, heterogeneous C-SLAM faces challenges in both accurate inter-robot loop closure detection and globally consistent data fusion due to inherent viewpoint disparities and heterogeneous data characteristics. This article introduces ATCM, an aerial–terrestrial light detection and ranging (LiDAR)-based C-SLAM method designed for heterogeneous robots without priori initial relative position. ATCM comprises three modules: single-robot front end employing diverse odometry methods, multi-robot loop closure detection, and global pose graph optimization (GPGO). A novel LiDAR-based cross-view global loop descriptor (CGLD) is proposed for scan-to-scan heterogeneous inter-robot loop closure detection. By uniformly mapping cross-view information into the height domain and integrating dynamic height, the loop descriptor automatically achieves viewpoint correction. In addition, we introduce a bidirectional loop detection (BLD) algorithm that validates inter-robot loop closures through both forward and reverse detections. Finally, the two-stage GPGO integrates multisource measurements, ensuring globally consistent mapping and localization with cross-view data. We have validated the effectiveness of ATCM on campus scenario datasets and the KITTI dataset, achieving a remarkable 21.95% improvement in trajectory accuracy and a 17.00% enhancement in map precision compared to high-precision point cloud maps, surpassing state-of-the-art LiDAR-based odometry methods. In the ablation experiments, the proposed loop descriptor achieved 97% accuracy in recognizing heterogeneous inter-robot loop closures. Moreover, compared to the traditional unidirectional method, the BLD method demonstrates up to a 31.2% improvement in loop closure accuracy.