DiSCo-SLAM: Distributed Scan Context-Enabled Multi-Robot LiDAR SLAM with Two-Stage Global-Local Graph Optimization

DiSCo-SLAM is the first distributed framework to use lightweight Scan Context descriptors for multi-robot SLAM, targeting 3D LiDAR observations and supporting efficient data exchange between robots. The framework includes two-stage global-local optimization and can provide stable localization results under unknown initial conditions for inter-robot loop closure search. Compared with the distributed Gauss-Seidel (DGS) method on multiple multi-robot datasets, its accuracy, stability, and data efficiency were quantitatively verified.