Evaluation of maps using fixed shapes: the fiducial map metric

Mapping is an important task for mobile robots. Assessing the quality of those maps is an open topic. 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. Those attributes are weighed to compute a final score depending on the application domain. During the 2010 NIST Response Robot Evaluation Exercise at Disaster City an area was populated with fiducials and different mapping runs were performed. The maps generated there are assessed in this paper demonstrating the Fiducial approach. Finally this map scoring algorithm is compared to other approaches found in literature.

[1]  Sebastian Thrun,et al.  Simultaneous Localization and Mapping , 2008, Robotics and Cognitive Approaches to Spatial Mapping.

[2]  Rolf Lakämper,et al.  Using virtual scans for improved mapping and evaluation , 2009, Auton. Robots.

[3]  Joachim Hertzberg,et al.  Ground truth evaluation of large urban 6D SLAM , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Afzal Godil,et al.  Map quality assessment , 2008, PerMIS.

[5]  Johannes Pellenz,et al.  Mapping and Map Scoring at the RoboCupRescue Competition , 2008 .

[6]  Arnoud Visser,et al.  Evaluating maps produced by urban search and rescue robots: lessons learned from RoboCup , 2009, Auton. Robots.

[7]  Adam Jacoff,et al.  DHS/NIST Response Robot Evaluation Exercises | NIST , 2007 .

[8]  Andreas Birk,et al.  Determining Map Quality through an Image Similarity Metric , 2009, RoboCup.

[9]  Hugh F. Durrant-Whyte,et al.  Simultaneous localization and mapping: part I , 2006, IEEE Robotics & Automation Magazine.

[10]  Paul Newman,et al.  Assessing Map Quality Using Conditional Random Fields , 2007, FSR.

[11]  Dana H. Ballard,et al.  Generalizing the Hough transform to detect arbitrary shapes , 1981, Pattern Recognit..

[12]  Andreas Birk,et al.  A quantitative assessment of structural errors in grid maps , 2010, Auton. Robots.

[13]  Cyrill Stachniss,et al.  On measuring the accuracy of SLAM algorithms , 2009, Auton. Robots.

[14]  Raj Madhavan,et al.  Quantitative Performance Evaluation of Navigation Solutions for Mobile Robots , 2008 .