Autonomous Robots

, Volume 21, Issue 2, pp 143–154 | Cite as

Relative localization using path odometry information

  • Nakju Lett Doh
  • Howie Choset
  • Wan Kyun Chung


All mobile bases suffer from localization errors. Previous approaches to accommodate for localization errors either use external sensors such as lasers or sonars, or use internal sensors like encoders. An encoder’s information is integrated to derive the robot’s position; this is called odometry. A combination of external and internal sensors will ultimately solve the localization error problem, but this paper focuses only on processing the odometry information. We solve the localization problem by forming a new odometry error model for the synchro-drive robot then use a novel procedure to accurately estimate the error parameters of the odometry error model. This new procedure drives the robot through a known path and then uses the shape of the resulting path to estimate the model parameters. Experimental results validate that the proposed method precisely estimates the error parameters and that the derived odometry error model of the synchro-drive robot is correct.


Relative localization Mobile robot Odometry error model Odometry calibration Synchro-drive robot Differential drive robot Generalized voronoi graph 


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  1. Adam, A., Rivlin, E., and Rostein. H. 1999. Fusion of fixation and odometry for vehicle navigation. IEEE Trans. on Systems, Man, and Cybernetics, 29(6):593–603.CrossRefGoogle Scholar
  2. Beer, F.P. and Johnston, E.R. 1994. Vector mechanics for engineers. McGraw Hill, pp. 344–345.Google Scholar
  3. Borenstein, J. and Feng. L. 1996a. Measurement and correction of systematic odometry errors in mobile robots. IEEE Trans. on Robotics and Automation, 12(6):869–880.CrossRefGoogle Scholar
  4. Borenstein, J. and Feng, L. 1996b. Gyrodometry: A new method for combining data from gyros and odometry in mobile robots. International Conference on Robotics and Automation, 423–428.Google Scholar
  5. Chong, K.S. and Kleeman, L. 1999. Mobile robot map building for an advanced sonar arrray and accurate odometry. International Journal of Robotics Research, 18(1):20–36.CrossRefGoogle Scholar
  6. Choset, H. and Nagatani, K. 2001. Topological SLAM: Toward exact localization without explicit localization. IEEE Trans. on Robotics and Automation, 17(2):125–137.CrossRefGoogle Scholar
  7. Chung, H., Ojeda, L., and Borenstein. J. 2001. Accurate mobile robot dead-recknoing with a precision-calibrated fiber-optic gyroscope. IEEE Trans. on Robotics and Automation, 17(1):80–84.CrossRefGoogle Scholar
  8. Duckett, D., Marsland, S., and Shapiro, J. 2000. Learning globally consistent maps by relaxation. International Conference on Robotics and Automation, 3841–3846.Google Scholar
  9. Fox, D., Burgard, W., and Thrun, S. 1999. Markov localization for mobile robots in dynamic environments. Journal of Artificial Intelligence, 11:391–427.zbMATHGoogle Scholar
  10. Jensfelt, P. and Kristensen, S. 2001. Active global localization for a mobile robot using myltiple hypothesis tracking. IEEE Trans. on Robotics and Automation, 17(5):748–760.CrossRefGoogle Scholar
  11. Kelly, A. 2004. Linearized error propagation in odometry. International Journal of Robotics Research, 23:179–218.CrossRefGoogle Scholar
  12. Kim, M.C., Chung, W.K., and Youm. Y. 1999. Posture estimation of car-like mobile robot using disturbance conditions. Advanced Robotics, 13(2):189–202.Google Scholar
  13. Kim, S.B., Choi, K., Lee, S., Choi, J., Hwang, T., Jang, B., and Lee, J. 2004. A bimodal approach for land vehicle localization. ETRI Journal, 26(5):497–500.Google Scholar
  14. Larsen, T.D., Bak, M., Andersen, N.A., and Ravn, O. 1998. Location estimation for autonomously guided vehicle using an augmented kalman filter to autocalibrate the odometry. FUSION 98, Spie conference Las Vegas.Google Scholar
  15. Martinelli, A. 2002. The odometry error of a mobile robot with a synchronous drive system. IEEE Trans. on Robotics and Automation, 18(3):399–405.CrossRefGoogle Scholar
  16. Martinelli, A., Tomatis, N., Tapus, A., and Siegwart, R. 2003. Simultaneous localization and odometry calibration for mobile robot. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, 1499–1504.Google Scholar
  17. Murata, S. and Hirose, T. 1993. Onboard locating system using real-time image processing for a self-navigating vehicle. IEEE Trans. on Industrial Electronics, 40(1):145–154.Google Scholar
  18. Roy, N. and Thrun, S. 1999. Online self-calibration for mobile robots. International Conference on Robotics and Automation, 2292–2297.Google Scholar
  19. Wang, C.M. 1988. Location estimation and uncertainty analysis for mobile robots. International Conference on Robotics and Automation, 1230–1235.Google Scholar

Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  • Nakju Lett Doh
    • 1
  • Howie Choset
    • 2
  • Wan Kyun Chung
    • 3
  1. 1.Intelligent Robot Research DivisionElectronics and Telecommunications Research InstituteKorea
  2. 2.Robotics InstituteCarnegie Mellon UniversityUSA
  3. 3.Department of Mechanical EngineeringPohang University of Science and TechnologyKorea

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