Mapping and Map Scoring at the RoboCupRescue Competition

The RoboCupRescue competition encourages the research on rescue robots: In a simulated disaster site, the task for the robots is to search for victims and to map the environment. The robots are either manually controlled or they explore autonomously. In addition to the number of victims,the quality of the maps and the mobility of the robots is evaluated and awarded in the competition. This paper gives an overview of the RoboCupRescue competition and the test arena with its standardized test element s. Also, the mapping and exploring technique used by team resko@UniKoblenz on the autonomous robot “Robbie X” is presented. Finally, the current map scoring at the RoboCupRescue competition is described and a method to score the maps automatically is proposed.

[1]  J. M. M. Montiel,et al.  The SPmap: a probabilistic framework for simultaneous localization and map building , 1999, IEEE Trans. Robotics Autom..

[2]  Kurt Konolige,et al.  Incremental mapping of large cyclic environments , 1999, Proceedings 1999 IEEE International Symposium on Computational Intelligence in Robotics and Automation. CIRA'99 (Cat. No.99EX375).

[3]  Sebastian Thrun,et al.  Learning Metric-Topological Maps for Indoor Mobile Robot Navigation , 1998, Artif. Intell..

[4]  Jiri Matas,et al.  Matching with PROSAC - progressive sample consensus , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[5]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[6]  S. Wirth,et al.  Exploration Transform: A stable exploring algorithm for robots in rescue environments , 2007, 2007 IEEE International Workshop on Safety, Security and Rescue Robotics.

[7]  Héctor H. González-Baños,et al.  Navigation Strategies for Exploring Indoor Environments , 2002, Int. J. Robotics Res..

[8]  Brian Yamauchi,et al.  A frontier-based approach for autonomous exploration , 1997, Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA'97. 'Towards New Computational Principles for Robotics and Automation'.

[9]  Alexander Zelinsky,et al.  Robot Navigation with Learning , 1988, Australian Computer Journal.

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

[11]  K. S. Arun,et al.  Least-Squares Fitting of Two 3-D Point Sets , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Zhengyou Zhang,et al.  Iterative point matching for registration of free-form curves and surfaces , 1994, International Journal of Computer Vision.

[13]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.