The Journal of Korea Robotics Society (로봇학회논문지)

Korea Robotics Society (한국로봇학회)
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Grid Map Building and Sample-based Data Association for Mobile Robot Equipped with Low-Cost IR Sensors

저가 적외선센서를 장착한 이동로봇에 적용 가능한 격자지도 작성 및 샘플기반 정보교합

  • 권태범 (고려대학교 기계공학부 지능로봇연구실) ;
  • 송재복 (고려대학교 기계공학부)
  • Received : 2009.03.23
  • Accepted : 2009.06.26
  • Published : 2009.08.31
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Abstract

Low-cost sensors have been widely used for mobile robot navigation in recent years. However, navigation performance based on low-cost sensors is not good enough to be practically used. Among many navigation techniques, building of an accurate map is a fundamental task for service robots, and mapping with low-cost IR sensors was investigated in this research. The robot's orientation uncertainty was considered for mapping by modifying the Bayesian update formula. Then, the data association scheme was investigated to improve the quality of a built map when the robot's pose uncertainty was large. Six low-cost IR sensors mounted on the robot could not give rich data enough to align the range data by the scan matching method, so a new sample-based method was proposed for data association. The real experiments indicated that the mapping method proposed in this research was able to generate a useful map for navigation.

Keywords

References

  1. J. S. Gutmann, W. Burgard, D. Fox and K. Konolige, "An experimental comparison of localization methods," Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 736-743, 1998.
  2. S. Thrun and M. Montemerlo, "The Graph SLAM Algorithm with Applications to Large-Scale Mapping of Urban Structures," International Journal of Robotics Research, vol. 25, pp. 403-429, 2006. https://doi.org/10.1177/0278364906065387
  3. M. Montemerlo, S. Thrun , D. Koller, B. Wegbreit, "FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem," Proc. of AAAI02, pp. 593-598, 2002.
  4. S. Thrun, "Learning Metric-Topological Maps for Indoor Mobile Robot Navigation," Artificial Intelligence, vol. 98, pp. 21-71, 1998.
  5. T. B. Kwon and J. B. Song, "Thinning-based Topological Exploration Using Position Probability of Topological Nodes," Advanced Robotics, vol. 22, pp. 339-359, 2008. https://doi.org/10.1163/156855308X292619
  6. H. Durrant-Whyte and T. Bailey, "Simultaneous Localization and Mapping: Part I, the essential algorithms," IEEE Trans. on Robotics and Automation Magazine, vol. 13, pp. 99-110, 2006.
  7. Y. J. Lee, T. B. Kwon and J. B. Song, "SLAM of a Mobile Robot Using Thinning-based Topological Information," International Journal of Control, Automation and Systems,vol. 5, pp. 557-583, 2007.
  8. L. Feng and M. Evangelos, "Globally consistent range scan alignment for environment mapping," Autonomous Robots, vol. 4, pp. 333-349, 1997. https://doi.org/10.1023/A:1008854305733
  9. D. Musicki, R. Evans and S. Stankovic, "Integrated Probabilistic Data Association," IEEE Trans. On Automatic Control, vol. 39, pp. 1237-1241, 1994. https://doi.org/10.1109/9.293185
  10. R. Siegwart and I. Nourbakhsh, Introduction to Autonomous Mobile Robots, The MIT Press, Cambridge, 2004.
  11. L. Feng and M. Evangelos, "Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans," Journal of Intelligent and Robotic Systems, vol. 18, pp. 249-275, 1997. https://doi.org/10.1023/A:1007957421070
  12. J. Borenstein, H. R. Everett and L. Feng, Where am I?, University of Michigan, http://www-personal.umich.edu/~johannb/shared/pos96rep.pdf, 1996.
  13. A. Elfes, "Using Occupancy grids for mobile robot perception and navigation," IEEE Journal of Computer, vol. 22, pp. 46-57, 1989.
  14. S. Thrun, W. Burgard and D. Fox, Probabilistic Robotics, The MIT Press, Cambridge, 2005.
  15. H. Choset, K. M. Lynch, S. Hutchinson, G. Kantor, W. Burgard, L. E. Kavraki and S. Thrun, Principles of Robot Motion: Theory, Algorithms, and Implementations, The MIT Press, Boston, 2005.