Information-based Active SLAM via Topological Feature Graphs
This work addresses scalability and drift issues in robot navigation and mapping, offering a domain-specific improvement for robotics.
The paper tackles the problem of active SLAM by proposing a Topological Feature Graph representation that scales better than occupancy grid maps, achieving higher accuracy while reducing computation and memory usage by orders of magnitude.
Active SLAM is the task of actively planning robot paths while simultaneously building a map and localizing within. Existing work has focused on planning paths with occupancy grid maps, which do not scale well and suffer from long term drift. This work proposes a Topological Feature Graph (TFG) representation that scales well and develops an active SLAM algorithm with it. The TFG uses graphical models, which utilize independences between variables, and enables a unified quantification of exploration and exploitation gains with a single entropy metric. Hence, it facilitates a natural and principled balance between map exploration and refinement. A probabilistic roadmap path-planner is used to generate robot paths in real time. Experimental results demonstrate that the proposed approach achieves better accuracy than a standard grid-map based approach while requiring orders of magnitude less computation and memory resources.