Antonio Ruiz-Mayor, Gracián Triviño, Gonzalo Bailador



This paper focus on the problem of how to measure in a reproducible way the localization precision of a mobile robot. In particular localization algorithms that match the classic prediction-correction model are considered. We propose a performance metric based on the formalization of the error sources that affect the pose estimation error. Performance results of a localization algorithm for a real mobile robot are presented. This metric fulfils at the same time the following properties: 1) to effectively measure the estimation error of a pose estimation algorithm, 2) to be reproducible, 3) to clearly separate the contribution of the correction part from the prediction part of the algorithm, and 4) to make easy the algorithm performance analysis respect to the great number of influencing factors. The proposed metric allows the validation and evaluation of a localization algorithm in a systematic and standard way, reducing workload and design time.


  1. Bloch, I. and Saffiotti, A., (2002), Why Robots should use Fuzzy Mathematical Morphology, Proc. 1st Int. ICSCNAISO Congress on Neuro-Fuzzy Technologies, La Havana, Cuba, January 2002. Retrieved may, 2006 from http://www.aass.oru.se/asaffio.
  2. Castellanos, J.A., Neira, J., and Tardós, J.D., (2001), Multisensor Fusion for Simultaneous Localization and Map Building, IEEE Transactions on Robotics and Automation, vol 17, no. 6, pp 908-914, dec. 2001.
  3. Clerentin, A., Delahoche, L., Brassart, E., Drocourt, C., (2005), Self localization: a new uncertainty propagation architecture, Robotics and Autonomous Systems 51 (2005) 151-166, Elsevier.
  4. Dillman, R., (2004), KA 1.10 Benchmarks for Robotics Research, Technical Report from EURON IST-2000- 26048 European Robotics Research Network, pp 1- 21. 24th April 2004. Retrieved May, 2005, from http://www.cas.kth.se/euron/euron-deliverables/ka1- 10-benchmarking.pdf
  5. Di Marco, M., Garulli, A., Giannitrapani, A., and Vicino, A., (2004), A Set Theoretic Approach to Dynamic Robot Localization and Mapping, Autonomous Robots 16, 23-47, Kluwer Academic Publishers, 2004.
  6. Fox, D., Burgard, W., Dellaert, F., and Thrun, S., (1999), Monte Carlo Localization: Efficient Position Estimation for Mobile Robots, Proc. 16th National Conf. on Artificial Intelligence (AAAI-99), pp 343- 349, Orlando, Florida, 1999.
  7. Gat, E., (1995), Towards principled experimental study of autonomous mobile robots, Autonomous Robots, vol 2 num 3, pp 179-189, Springer, 1995.
  8. Gutmann, J.S., and Fox, D., (2002), An Experimental Comparison of Localization Methods Continued, Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, IROS'02, vol I, pp454-9, Piscataway NJ, USA, 2002.
  9. Hanks, S., Pollack M.E., Cohen, P.R., (1993), Benchmarks, Testbeds, Controlled Experimentation, and the design of Agent Architectures, AI Magazine, 14(4):17-42, 1993.
  10. Lee, S. Song, J.B., (2004) Mobile Robot Localization Using Optical Flow Sensors, International Journal of Control, Automation, and Systems, vol.2, no.4, pp. 485-493, December 2004.
  11. Meystel, A., Albus, J., Messina, E., and Leedom D., (2003), Performance Measures for Intelligent Systems, Performance Metrics for Intelligent Systems PerMIS'03, NIST Special Publication 1014, NIST, September 16-18, 2003.
  12. O'Sullivan, S., Collins, J.J., Mansfield, M., Haskett, D., and Eaton, M., (2004), A Quantitative evaluation of sonar models and mathematical update methods for Map Building with mobile robots, Proc. 9th Int. Symposium on Artificial Life and Robotics AROB 2004. Retrieved February 24, 2006, from http://www.skynet.ie/sos/masters/ArobPapers/Sonar ModelsMathsAROB2004Paper.pdf
  13. Porta, J.M., Verbeek, J.J., and Kröse, B.J.A., (2005) Active Appearance-Based Robot Localization Using Stereo Vision, Autonomous Robots 18, 59-80, Springer Science + Business Media, Inc., 2005.
  14. Sagüés, C., Guerrero, J.J., (2005), Visual correction for mobile robot homing, Robotics and Autonomous Systems 50 (2005) 41-49, Elsevier.
  15. Thrun, S. (2002), Robotic Mapping: A Survey. In Exploring Artificial Intelligence in the New Millenium, Lakemeyer, G. and Nebel, B. (eds), Morgan Kaufmann.
  16. Welch, G. and Bishop, G. (2004, April 5), An Introduction to the Kalman Filter, ref. TR 95-041, University of North Carolina at Chapel Hill, Retrieved February 24, 2006, from http://www.cs.unc.edu/welch/kalman/kalmanIntro.ht ml

Paper Citation

in Harvard Style

Ruiz-Mayor A., Triviño G. and Bailador G. (2006). A PERFORMANCE METRIC FOR MOBILE ROBOT LOCALIZATION . In Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-972-8865-60-3, pages 269-276. DOI: 10.5220/0001218602690276

in Bibtex Style

author={Antonio Ruiz-Mayor and Gracián Triviño and Gonzalo Bailador},
booktitle={Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},

in EndNote Style

JO - Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
SN - 978-972-8865-60-3
AU - Ruiz-Mayor A.
AU - Triviño G.
AU - Bailador G.
PY - 2006
SP - 269
EP - 276
DO - 10.5220/0001218602690276