Sparse Hierarchical LiDAR Bundle Adjustment for Online Collaborative Localization and Mapping

This letter presents a sparse hierarchical LiDAR bundle adjustment method for online multi-robot collaborative simultaneous localization and mapping (C-SLAM). The motivation behind this work is that the pose graph cannot directly reflect map inconsistencies. As a result, the map divergence across multiple robots persists even after pose graph optimization. While existing methods have utilized LiDAR bundle adjustment (BA) to address the divergence issues, none of these methods are particularly effective at improving online localization accuracy in multi-robot scenarios. In this work, we propose a sparse hierarchical mechanism where LiDAR bundle adjustment functions as a low-latency module within a centralized C-SLAM system. The hierarchical design accelerates the original BA process, while sparse selection further decreases the problem's complexity, thereby improving computational efficiency. The combination of high-frequency multi-robot pose graph optimization (MR-PGO) and low-frequency multi-robot hierarchical bundle adjustment (MR-HBA) improves accuracy and provides real-time localization results. To validate the effectiveness of our proposed method, we conduct comparative experiments using multiple public datasets and a self-collected dataset, benchmarking against state-of-the-art multi-robot SLAM systems. The results demonstrate that our method significantly enhances the accuracy of localization and mapping. Additionally, we have made the entire system available as an open-source implementation to benefit the broader research community.