LOCALIZATION WITH DYNAMIC MOTION MODELS - Determining Motion Model Parameters Dynamically in Monte Carlo Localization

Adam Milstein, Tao Wang

Abstract

Localization is the problem of determining a robot’s location in an environment. Monte Carlo Localization (MCL) is a method of solving this problem by using a partially observable Markov decision process to find the robot’s state based on its sensor readings, given a static map of the environment. MCL requires a model of each sensor in order to work properly. One of the most important sensors involved is the estimation of the robot’s motion, based on its encoders that report what motion the robot has performed. Since these encoders are inaccurate, MCL involves using other sensors to correct the robot’s location. Usually, a motion model is created that predicts the robot’s actual motion, given a reported motion. The parameters of this model must be determined manually using exhaustive tests. Although an accurate motion model can be determined in advance, a single model cannot optimally represent a robot’s motion in all cases. With a terrestrial robot the ground surface, slope, motor wear, and possibly tire inflation level will all alter the characteristics of the motion model. Thus, it is necessary to have a generalized model with enough error to compensate for all possible situations. However, if the localization algorithm is working properly, the result is a series of predicted motions, together with the corrections determined by the algorithm that alter the motions to the correct location. In this case, we demonstrate a technique to process these motions and corrections and dynamically determine revised motion parameters that more accurately reflect the robot’s motion. We also link these parameters to different locations so that area dependent conditions, such as surface changes, can be taken into account. These parameters might even be used to identify surface changes by examining the various parameters. By using the fact that MCL is working, we have improved the algorithm to adapt to changing conditions so as to handle even more complex situations.

References

  1. A. Milstein. 2005. Dynamic Maps in Monte Carlo Localization. In 18th Canadian Conference on Artificial Intelligence.
  2. A. Milstein, J. Sanchez, and E.Williamson. 2002. Robust global localization using clustered particle filtering. In AAAI-02.
  3. D. Avots, E. Lim, R. Thibaux, and S. Thrun. A probabilistic technique for simultaneous localization and door state estimation with mobile robots in dynamic environments. In IROS-2002.
  4. Thrun, S. 2000. Probabilistic Algorithms in Robotics. School of Computer Science, Carnegie Mellon University. Pittsburgh, PA.
  5. M. Montemerlo, S. Thrun, D. Koller, and B. Wegbreit. 2002. FastSLAM: A factored solution to the simultaneous localization and mapping problem. In AAAI-02.
  6. Thrun, S.; Fox, D.; Burgard, W.; and Dellaert, F. 2001. Robust Monte Carlo Localization for Mobile Robots. Artificial Intelligence Magazine.
  7. J.Liu and R. Chen. 1998. Sequential monte carlo methods for dynamic systems. Journal of the American Statistical Association 93:1032-1044.
  8. Borenstein, J.; Everett, B.; and Feng, L. 1996. Navigating Mobile Robots: Systems and Techniques. A.K. Peters, Ltd. Wellesley, MA.
  9. Thrun, S.; Fox, D.; and Burgard, W. 2000. Monte Carlo Localization with Mixture Proposal Distribution. In Proceedings of the AAAI National Conference on Artificial Intelligence, Austin, TX.
  10. Thrun, S. 2002; Particle Filters in Robotics. In Proceedings of Uncertainty in AI 2002.
  11. M. I. Jordan, Z. Ghahramani, T. S. Jaakkola, and L. K. Saul. In M. I. Jordan (Ed.); An introduction to variational methods for graphical models. Learning in Graphical Models, Cambridge: MIT Press, 1999.
  12. Fox, D.; Burgard, W. and Thrun, S.; Markov Localization for Mobile Robots in Dynamic Environments. In Journal of Artificial Intelligence Research, 1999.
  13. Hähnel, D.; Triebel, R.; Burgard, W. and Thrun, S.; Map building with mobile robots in dynamic environments. In ICRA, 2003.
  14. Thrun, S.; Burgard W.; Fox, D.; Probabilistic Robotics. Cambridge: MIT Press, 2005.
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Paper Citation


in Harvard Style

Milstein A. and Wang T. (2006). LOCALIZATION WITH DYNAMIC MOTION MODELS - Determining Motion Model Parameters Dynamically in Monte Carlo 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 120-127. DOI: 10.5220/0001204501200127


in Bibtex Style

@conference{icinco06,
author={Adam Milstein and Tao Wang},
title={LOCALIZATION WITH DYNAMIC MOTION MODELS - Determining Motion Model Parameters Dynamically in Monte Carlo Localization},
booktitle={Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2006},
pages={120-127},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001204501200127},
isbn={978-972-8865-60-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - LOCALIZATION WITH DYNAMIC MOTION MODELS - Determining Motion Model Parameters Dynamically in Monte Carlo Localization
SN - 978-972-8865-60-3
AU - Milstein A.
AU - Wang T.
PY - 2006
SP - 120
EP - 127
DO - 10.5220/0001204501200127