Mobile Robot Localization via Outlier Rejection in Sonar Range Sensor Data

Year 2012, Volume: 12 Issue: 1, 1457 - 1464, 02.09.2013

Sezcan Yılmaz , Hilal Kayır Burak Kalecı Osman Parlaktuna

Abstract

Localization is an important ability for a mobile robot. The probabilistic localization methods become more popular because of the ability of representing the uncertainties of the sensor measurements and inaccuracies in environments. They also provide robust solutions for different localization problems. The particle filter is one of the probabilistic localization methods. In this study, sonar range sensors are used for mobile robot localization. Sonar range sensors suffer from wrong reflections that may result outliers in the data set.  Outliers may also occur in the particle filter process. In this study, a new sensor model Repealing Range Sensor Model (R²SM) is proposed and integrated to particle filter to reduce the effects of outliers. In order to show the effectiveness of the proposed method, experiments are conducted and the results are compared with a well-known outlier rejection method, Grubbs' T-Test. Experiments show that results of the proposed approach are comparable to the results of the Grubbs' T-Test in terms of Localization Success Ratio (LSR) and Number of Iterations (NOI) required for localization. The main advantage of the proposed R2SM is that it does not require any additional information such as critical value table. This provides more flexible outlier rejection approach.

Keywords

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References

• F. Lu and E. Milios, “Globally consistent range scan alignment for environment mapping”, Autonomous Robots, vol. 43, pp. 333-349, 1997.
• S. Mahadevan and N. Khalceli, “Robust mobile robot navigation using partially-observable semi-Markov decision processes”, Internal Report, 1999.
• F. Dellaert, D. Fox, W. Burgard, S. Thrun, “Monte Carlo Localization for mobile robots”, In Proc. of the International Automation, 1999. on Robotics and D. Fox, W. Burgard, F. Dellaert, S. Thrun, “Monte
• Carlo Localization: Efficient Position Estimation for Mobile Robot”, Proc. of the Sixteenth National Conference on Artificial Intelligence, Orlando, Florida, 1999.
• H. Choset, K. M. Lynch, S. Hutchinson, G. Kantor, W. Burgard, L. E. Kavraki, and S. Thrun, “Principles of Robot Motion Planning”, MIT Press, 2005.
• S. Thrun, W. Burgard, and D. Fox, “Probabilistic Robotics”, MIT Press, 2005.
• M. K. Pitt and N. Shephard, “Filtering via simulation: auxilary particle filters”, Journal of American Statistical Association, 94(446), 1999.
• S. Thrun, D. Fox, W. Burgard, and F. Delleart, “Robust Monte Carlo Localization for mobile robots,” Artificial Intelligence, 128(1-2), 2001.
• K. Lee, N. L. Doh and W. K. Chung, “An Exploration Sonar Strategy Environments” Intel Serv Robotics, Vol 3, pp.89-98, Sensors in Corridor P. Zingaretti and E. Frontoni, “Vision and sonar sensor fusion for mobile robot localization in aliased environments”, IEEE/ASME International on Mechatronic Applications, Beijing, China, pp.1-6, 2006. nd Conference Embedded Systems and J. Vaganay, J. J. Leonard, and J. G. Bellingham,
• “Outlier Rejection for Autonomous Acoustic Navigation”, In Proc. of the International Conference on Robotics and Automation, Minnesota, USA, pp. 2181,1999.
• N. Vlassis, B. Terwijn, and B. Kröse, “Auxiliary Particle Filter Robot Localization High-Dimensional Sensor Observation”, In Proc. of the International Conference Robotics Washington, USA, pp. 7-12,2002. and Automation,
• E. Olson, J. J. Leonard, and S. Teller, “Robust Range- Only Beacon Localization”, IEEE Journal of Oceanic Engineering, Vol. 31, No. 4, pp. 949-958, 2006.
• K. E. Bekris, M. Glick, and L. E. Kavraki, “Evaluation of Algorithms for Bearing-Only SLAM”, In Proc. of the International Conference on Robotics and Automation, Florida, USA, pp. 1937-1943,2006.
• T. B. Kwon, J. H. Yang, J. B. Song, and W. Chung, “Efficiency Improvement in Monte Carlo Localization through Topological Information”, In Proceedings of the International Conference on Intelligent Robots and Systems (IROS), Beijing, China, 2006.
• S. Thrun, D. Fox, and W. Burgard, “Monte Carlo Localization With Mixture Proposal Distribution”, In Proceedings of the AAAI National Conference on Artificial Intelligence, Austin, TX, 2000.
• H. Czichos , T. Saito, and L. M. Smith, “Handbook of Materials Measurement Methods”, Springer, 2006. www.mobilerobots.com, 2012.
• Sezcan YILMAZ received the BA Mechanical Engineering from the University (ESOGU), Eskisehir, Turkey. He has been working at the Department of Mechanical Engineering of the ESOGU since 2004. Currently, he is in pursuing