Lost Robot Self-Recovery via Exploration Using Hybrid Topological-Metric Maps

Abstract

A robot might get lost due to abrupt wheel slippage, unsteady movements on uneven floors, collision with obstacles, blocked perception sensors or kidnapping. When this happens, the robot has to recover by itself. This is necessary but has not been satisfactorily resolved. In this paper, a generic lost robot self-recovery framework is proposed. It has self-exploration capability assisted by an efficient place recognition module using hybrid topological-metric maps. The metric map is used for path planning and navigation while the topological map is used for re-localization when lost. As soon as the robot detects that it is lost, self exploration is activated and starts to explore while performing place recognition using the topological map. Once the place is re-identified, the proximate global location is obtained and the robot performs fine localization using the metric map, recovery itself and continue to its destination. The proposed system has been implemented on a mobile robot operating in a typical office environment. Experiments conducted show a robot can be efficiently and reliably recover itself when it gets lost within or outside of the map.