Using a fiducial map metric for assessing map quality in the context of RoboCup Rescue

Mapping is an important task for mobile robots in general and for Safety, Security, and Rescue Robotics (SSRR) in particular. It is hence one core aspect which is evaluated in the RoboCup Rescue league. But assessing the quality of maps in a simple and efficient way is not trivial, especially if no detailed, complete ground truth data of the environment is available. A new approach on map evaluation is presented here. It makes use of artificial objects placed in the environment named “fiducials”. Using the known ground-truth positions and the positions of the fiducials identified in the map, a number of quality attributes can be assigned to that map. Depending on the application domain those attributes can weighed to compute a final score. Results are presented that are based on using this method during the RoboCup Rescue competition 2010 in Singapore where maps were generated by different teams in an maze populated with fiducials. Those maps are evaluated here and compared to a human judgment, showing the effectiveness of the fiducial approach.