Landmark-Tree map: A biologically inspired topological map for long-distance robot navigation

Abstract

Metric maps provide a reliable basis for mobile robot navigation. However, such maps are in general quite resource expensive and do not scale very well. Aiming for a highly scalable map, we adopt theories of insect navigation to develop an algorithm which builds a topological map for global navigation. Similar to insect conduct, positions in space are memorized as snapshots, which are unique configurations of landmarks. Unlike conventional snapshot approaches, we do not simply store the landmarks as a set, but we build a landmark tree which enables us to easily free memory in case of a continuously growing map while still preserving the dominant information. The resulting navigation is not sensor specific and solely relies on the directions of arbitrary landmarks. The generated map enables a mobile robot to navigate between defined locations and let it retrace a previously pursued path. Finally, we verify the reliability of the Landmark-Tree Map (LT-Map) concept and its robustness on memory limitations.