Maintaining topological maps for mobile robots

Nowadays, mobile robots solve various tasks realted to navigation in an unknown or changing environment. In such conditions, mapping the environment is crucial for mobile robot navigation. Conventionally, maps are built as 2D or 3D dense metric structures which require much memory for storage and much computational time for path planning, especially in large environments. Representing a map as a topological structure (i.e. a graph of locations) allows fast path planning with low memory consumption. In this paper, we present a method of real-time topological map building and updating from odometry measurements and local point clouds. The proposed method guarantees the topological graph connectivity and adds shortcuts to the graph to optimize paths. We tested our method in a simulated environment and measured efficiency of path planning in the obtained graphs. In all the tests, the SPL value exceeded 81%, with 100% success rate. The source code of our method is available at https://github.com/KirillMouraviev/simple_toposlam_model.