Low Data Overlap Rate Graph-Based SLAM with Distributed Submap Strategy

Simultaneous localization and mapping (SLAM) is widely used in many robot applications to acquire the unknown environment’s map and the robot’s location. Graph-based SLAM is demonstrated to be effective in large-scale scenarios, and it intuitively performs the SLAM as a pose graph. But because of the high data overlap rate, traditional graph-based SLAM is not efficient in some respects, such as real time performance and memory usage. To reduce data overlap rate, a graph-based SLAM with distributed submap strategy (DSS) is presented. In its front-end, submap based scan matching is processed and loop closing detection is conducted. Moreover in its back-end, pose graph is updated for global optimization and submap merging. From a series of experiments, it is demonstrated that graph-based SLAM with DSS reduces 51.79% data overlap rate, decreases 39.70% runtime and 24.60% memory usage. The advantages over other low overlap rate method is also proved in runtime, memory usage, accuracy and robustness performance.