A quantitative assessment of structural errors in grid maps
Autonomous Robots
PerMIS '08 Proceedings of the 8th Workshop on Performance Metrics for Intelligent Systems
Performance measures framework for unmanned systems (PerMFUS): initial perspective
PerMIS '09 Proceedings of the 9th Workshop on Performance Metrics for Intelligent Systems
Evaluating the RoboCup 2009 Virtual Robot Rescue Competition
PerMIS '09 Proceedings of the 9th Workshop on Performance Metrics for Intelligent Systems
Evaluation of maps using fixed shapes: the fiducial map metric
Proceedings of the 10th Performance Metrics for Intelligent Systems Workshop
Towards evaluating world modeling for autonomous navigation in unstructured and dynamic environments
Proceedings of the 10th Performance Metrics for Intelligent Systems Workshop
A hybrid approach to 2D robotic map evaluation
Proceedings of the Workshop on Performance Metrics for Intelligent Systems
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A quantitative assessment of the quality of a robot generated map is of high interest for many reasons. First of all, it allows individual researchers to quantify the quality of their mapping approach and to study the effects of system specific choices like different parameter values in an objective way. Second, it allows peer groups to rank the quality of their different approaches to determine scientific progress; similarly, it allows rankings within competition environments like RoboCup. A quantitative assessment of map quality based on an image similarity metric 驴 is introduced here. It is shown through synthetic as well as through real world data that the metric captures intuitive notions of map quality. Furthermore, the metric is compared to a seemingly more straightforward metric based on Least Mean Squared Euclidean distances (LMS-ED) between map points and ground truth. It is shown that both capture intuitive notions of map quality in a similar way, but that 驴 can be computed much more efficiently than the LMS-ED.