Increasing the SLAM performance by integrating the grid-topology based hybrid map and the adaptive control method

격자위상혼합지도방식과 적응제어 알고리즘을 이용한 SLAM 성능 향상

  • 김수현 (목원대 IT공학과, 국립암센터) ;
  • 양태규 (목원대 지능로봇공학과)
  • Published : 2009.08.01

Abstract

The technique of simultaneous localization and mapping is the most important research topic in mobile robotics. In the process of building a map in its available memory, the robot memorizes environmental information on the plane of grid or topology. Several approaches about this technique have been presented so far, but most of them use mapping technique as either grid-based map or topology-based map. In this paper we propose a frame of solving the SLAM problem of linking map covering, map building, localizing, path finding and obstacle avoiding in an automatic way. Some algorithms integrating grid and topology map are considered and this make the SLAM performance faster and more stable. The proposed scheme uses an occupancy grid map in representing the environment and then formulate topological information in path finding by A${\ast}$ algorithm. The mapping process is shown and the shortest path is decided on grid based map. Then topological information such as direction, distance is calculated on simulator program then transmitted to robot hardware devices. The localization process and the dynamic obstacle avoidance can be accomplished by topological information on grid map. While mapping and moving, pose of the robot is adjusted for correct localization by implementing additional pixel based image layer and tracking some features. A laser range finer and electronic compass systems are implemented on the mobile robot and DC geared motor wheels are individually controlled by the adaptive PD control method. Simulations and experimental results show its performance and efficiency of the proposed scheme are increased.

Keywords

References

  1. A. Elfes 'Using Occupancy Grid' IEEE Journal of Computer, Vol. 22, No. 6 pp 46-57, 1989
  2. B.Kuipers, Y.T. Byun, 'A robot exploration and mapping strategy based on a semantic hierarchy of spatial representations,' Journal of Robotics and Autonomous Systems, 1991 https://doi.org/10.1016/0921-8890(91)90014-C
  3. S. Thrun, 'Learning maps for indoor mobile robot navigation,' Artificial Intelligence, 1999
  4. Tae-Bum Kwon, Jae-Bok Song, 'Thinning-based topological map building for local and global environments,' Journal of Control, Automation and Systems Engineering, Vol. 12, No.7, July 2006
  5. G. Weiss, E. Puttkamer, 'A map based on laser scans without geometric interpretation,' Intelligent Autonomous Systems, pp. 403-407, IOS Press, 1995
  6. M. Skubic, G. Chronis, P. Matsakis, J.M. Keller, 'Generating linguistic spatial descriptions from sonar reading using the histogram of forces,' Proc. 2001 IEEE Int. Conf. on Robotics and Automations, Seoul, Korea, 2001
  7. J. J. Leonard and H. F. Durrant-Whyte, 'Directed sonar sensing for Mobile Robot Navigation,' Kluwer Academic Publishers, Boston, MA 1992
  8. W.D.D.and T.hmidt. 'Estimating the absolute position of a mobile robot using position probability grids,' Proc. of the National Conference on Artificial Intelligence (AAAI), 1996
  9. Thomas Rufer, 'Using histogram correlation to create consistent laser scan maps,' Proc. of the IEEE International Conference on Robotics Systems(IROS), EPFL, Lausanne, Switzerland, pp. 625-630, 2002
  10. Agostino Martinelli et al, 'A relative map approach to SLAM based on shift and rotation invariants,' Robotics and Autonomous Systems, Vol.55, pp.50-61, 2007 https://doi.org/10.1016/j.robot.2006.06.009
  11. C. C. Wang and C. Thorpe. 'Simultaneous localization and mapping with detection and tracking of moving objects,' Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2002
  12. M.Begum, G.K.I. Mann, R.G.Gosine, 'Integrated fuzzy logic and genetic algorithmic approach for simultaneous localization and mapping of mobile robots,' Applied Soft Computing, Vol. 8, pp. 150-165, 2008 https://doi.org/10.1016/j.asoc.2006.11.010