A fast, accurate and robust method for self-localization in polygonal environments using laser range finders

Self-localization is important in almost all robotic tasks. For playing an aesthetic and effective game of robotic soccer, self-localization is a necessary prerequisite. When we designed our robotic soccer team for participating in robotic soccer competitions, it turned out that none of the existing approaches met our requirements of being fast, accurate and robust. For this reason, we developed a new method, which is presented and analyzed in this paper. This method is one of the key components and is probably one of the explanations for the success of our team in national and international competitions. We also present experimental evidence that our method outperforms other self-localization methods in the RoboCup environment.

[1]  Wolfram Burgard,et al.  An experimental comparison of localization methods , 1998, Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190).

[2]  Hiroaki Kitano,et al.  RoboCup-99: Robot Soccer World Cup III , 2003, Lecture Notes in Computer Science.

[3]  Wolfram Burgard,et al.  Integrating global position estimation and position tracking for mobile robots: the dynamic Markov localization approach , 1998, Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190).

[4]  Evangelos E. Milios,et al.  Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Gerhard Weiss,et al.  A map based on laserscans without geometric interpretation , 1999 .

[6]  Ingemar J. Cox,et al.  Blanche-an experiment in guidance and navigation of an autonomous robot vehicle , 1991, IEEE Trans. Robotics Autom..

[7]  Wolfram Burgard,et al.  Estimating the Absolute Position of a Mobile Robot Using Position Probability Grids , 1996, AAAI/IAAI, Vol. 2.

[8]  Javier Gonzalez,et al.  Comparison of two range-based pose estimators for a mobile robot , 1993, Other Conferences.

[9]  Bernhard Nebel,et al.  Navigation mobiler Roboter mit Laserscans , 1997, AMS.

[10]  José A. Castellanos,et al.  Constraint-based mobile robot localization , 1996 .

[11]  Evangelos E. Milios,et al.  Globally Consistent Range Scan Alignment for Environment Mapping , 1997, Auton. Robots.

[12]  Hiroaki Kitano,et al.  RoboCup-97: Robot Soccer World Cup I , 1998, Lecture Notes in Computer Science.

[13]  P. S. Maybeck,et al.  The Kalman Filter: An Introduction to Concepts , 1990, Autonomous Robot Vehicles.

[14]  Hiroaki Kitano,et al.  RoboCup: A Challenge Problem for AI , 1997, AI Mag..

[15]  W. Whittaker,et al.  Position estimator for underground mine equipment , 1992 .

[16]  北野 宏明,et al.  RoboCup-97 : robot soccer World Cup I , 1998 .

[17]  Hiroaki Kitano,et al.  RoboCup-98: Robot Soccer World Cup II , 2001, Lecture Notes in Computer Science.

[18]  J.-S. Gutmann,et al.  AMOS: comparison of scan matching approaches for self-localization in indoor environments , 1996, Proceedings of the First Euromicro Workshop on Advanced Mobile Robots (EUROBOT '96).

[19]  Bernhard Nebel,et al.  The CS Freiburg 99 Team , 1999 .

[20]  Bernhard Nebel,et al.  The CS Freiburg Robotic Soccer Team: Reliable Self-Localization, Multirobot Sensor Integration, and Basic Soccer Skills , 1998, RoboCup.