Comparison of Self-Localization Methods for Soccer Robots

This paper presents a comparison of two localization algorithms for tiny autonomous robots in a well known but highly dynamic environment. Position knowledge is gained through collecting information about distinctive features in the environment with a stereo vision system and comparing it to a model of the world. The model consists of a map of the static environment and information about moving objects. Based upon this model, the sensor data is used to generate a hypothesis of the position of the robot in the real world. A robot soccer game played by small, autonomous robots is the test-bed for this work. Constraints such as small size of the robot and the dynamic nature of the environment has to be taken into account while developing any solution for the localization. Simulation results show that for the given application, the extended Kalman filter based method is comparable in performance to the particle filter based method, although the particle filter has high time complexity.

[1]  S. Mahlknecht,et al.  TINYPHOON A Tiny Autonomous Mobile Robot , 2005, Proceedings of the IEEE International Symposium on Industrial Electronics, 2005. ISIE 2005..

[2]  Dieter Fox,et al.  Adapting the Sample Size in Particle Filters Through KLD-Sampling , 2003, Int. J. Robotics Res..

[3]  Axel Großmann,et al.  Goal Recognition with Variable-Order Markov Models , 2009, IJCAI.

[4]  Stefano Cagnoni,et al.  Landmark-based robot self-localization: a case study for the RoboCup goal-keeper , 1999, Proceedings 1999 International Conference on Information Intelligence and Systems (Cat. No.PR00446).

[5]  Jason Jianjun Gu,et al.  Active Single Landmark Based Global Localization of Autonomous Mobile Robots , 2006, ISVC.

[6]  Hugh F. Durrant-Whyte,et al.  Natural landmark-based autonomous vehicle navigation , 2004, Robotics Auton. Syst..

[7]  A. Bais,et al.  Location tracker for a mobile robot , 2007, 2007 5th IEEE International Conference on Industrial Informatics.

[8]  Raúl Rojas,et al.  Analysis by Synthesis, a Novel Method in Mobile Robot Self-Localization , 2004, RoboCup.

[9]  Emanuele Menegatti,et al.  Image-based Monte Carlo localisation with omnidirectional images , 2004, Robotics Auton. Syst..

[10]  Ching-Chih Tsai.  A localization system of a mobile robot by fusing dead-reckoning and ultrasonic measurements , 1998, IEEE Trans. Instrum. Meas..

[11]  J. L. Roux.  An Introduction to the Kalman Filter , 2003 .

[12]  Jean-Yves Tourneret,et al.  A Rao-Blackwellized particle filter for INS/GPS integration , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[13]  Drew McDermott,et al.  Error correction in mobile robot map learning , 1992, Proceedings 1992 IEEE International Conference on Robotics and Automation.

[14]  R. Sablatnig,et al.  Line-based landmark recognition for self-localization of soccer robots , 2005, Proceedings of the IEEE Symposium on Emerging Technologies, 2005..

[15]  Dieter Fox,et al.  Markov localization - a probabilistic framework for mobile robot localization and navigation , 1998 .

[16]  Abdul Bais,et al.  Landmark Based Global Self-localization of Mobile Soccer Robots , 2006, ACCV.

[17]  Dieter Fox,et al.  An experimental comparison of localization methods continued , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[18]  Dieter Fox,et al.  Real-time particle filters , 2004, Proceedings of the IEEE.

[19]  Wolfram Burgard,et al.  Monte Carlo localization for mobile robots , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[20]  Jurjen Caarls,et al.  A Two-Tiered Approach to Self-Localization , 2001, RoboCup.

[21]  Roland Siegwart,et al.  Feature-based multi-hypothesis localization and tracking using geometric constraints , 2003, Robotics Auton. Syst..

[22]  Tsutomu Hasegawa,et al.  Self-localization Method Using Two Landmarks and Dead Reckoning for Autonomous Mobile Soccer Robots , 2003, RoboCup.

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

[24]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.