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

Abstract

In order to improve the accuracy and efficiency of simultaneous localization and mapping (SLAM) in large, degraded, and complex areas, real-time multi-robot 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 inter-robot loop constraints lead to insufficient robustness. On the other hand, the bandwidth occupation is large in inter-robot loop areas. Therefore, in order to solve the above problems, we innovatively propose the distributed robust and communication-efficient multi-robot cooperative LiDAR-inertial SLAM (DRCM-CSLAM). Firstly, the FAST-LIO2 with loop detection and loop correction is integrated into 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 inter-robot loop constraints. Finally, we are the first to combine Scan Context (SC) descriptor and incremental octree to design a lightweight and efficient communication strategy, significantly reducing bandwidth occupation of inter-robot loop areas and ensuring real-time performance. The experiments are tested on KITTI datasets and collected datasets. The results show that our method has the 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%.