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Determining Map Quality through an Image Similarity Metric

  • Ioana Varsadan
  • Andreas Birk
  • Max Pfingsthorn
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5399)

Abstract

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 Ψ 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 Ψ can be computed much more efficiently than the LMS-ED.

Keywords

Ground Truth Mobile Robot Image Similarity Ground Truth Data Test Element 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Ioana Varsadan
    • 1
  • Andreas Birk
    • 1
  • Max Pfingsthorn
    • 1
  1. 1.Jacobs University BremenBremenGermany

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