DRCM-CSLAM: Distributed Robust and Communication-Efficient Multirobot Cooperative LiDAR-Inertial SLAM

In order to improve the accuracy and efficiency of simultaneous localization and mapping (SLAM) in large, degraded, and complex areas, real-time multirobot cooperative SLAM has more obvious advantages in accuracy, fault tolerance, and flexibility than single robot SLAM. However, currently, existing cooperative SLAM algorithms have the following issues. On the one hand, poor localization of degraded scenes and incomplete consideration of interrobot loop constraints lead to insufficient robustness. On the other hand, the bandwidth occupation is large in interrobot loop areas. Therefore, in order to solve the above problems, we innovatively propose the distributed robust and communication-efficient multirobot cooperative LiDAR-inertial SLAM (DRCM-CSLAM). First, the FAST-LIO2 with loop detection and loop correction is integrated into a cooperative framework to improve the robustness in degraded scenes. Subsequently, a two-stage loop filtering is proposed to improve the accuracy of the two-stage optimization of DiSCo-SLAM by fully utilizing interrobot loop constraints. Finally, we are the first to combine the scan context (SC) descriptor and incremental octree to design a lightweight and efficient communication strategy, significantly reducing bandwidth occupation of interrobot loop areas and ensuring real-time performance. The experiments are tested on KITTI datasets and collected datasets. The results show that our method has superior performance in robustness, bandwidth, and runtime. Compared with DiSCo-SLAM, our method reduces absolute translational error (RMSE) by 42.9% and bandwidth by 90%.