Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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2D Grid Map Compensation Using ICP Algorithm based on Feature Points

특징 점 기반의 ICP 알고리즘을 이용한 2차원 격자지도 보정

  • Hwang, Yu-Seop (Department of Electronic and Electric and Computer Engineering, Pusan National University) ;
  • Lee, Dong-Ju (Department of Electronic and Electric and Computer Engineering, Pusan National University) ;
  • Yu, Ho-Yun (Department of Electronic and Electric and Computer Engineering, Pusan National University) ;
  • Lee, Jang-Myung (Department of Electronic and Electric and Computer Engineering, Pusan National University)
  • 황요섭 (부산대학교 전자전기컴퓨터공학과) ;
  • 이동주 (부산대학교 전자전기컴퓨터공학과) ;
  • 유호윤 (부산대학교 전자전기컴퓨터공학과) ;
  • 이장명 (부산대학교 전자전기컴퓨터공학과)
  • Received : 2014.12.23
  • Accepted : 2015.09.02
  • Published : 2015.10.01
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Abstract

This paper suggests a feature point-based Iterative Closest Point (ICP) algorithm to compensate for the disparity error in building a two-dimensional map. The ICP algorithm is a typical algorithm for matching a common object in two different images. In the process of building a two-dimensional map using the laser scanner data, warping and distortions exist in the map because of the disparity between the two sensor values. The ICP algorithm has been utilized to reduce the disparity error in matching the scanned line data. For this matching process in the conventional ICP algorithm, pre-known reference data are required. Since the proposed algorithm extracts characteristic points from laser-scanned data, reference data are not required for the matching. The laser scanner starts from the right side of the mobile robot and ends at the left side, which causes disparity in the scanned line data. By finding the matching points between two consecutive frame images, the motion vector of the mobile robot can be obtained. Therefore, the disparity error can be minimized by compensating for the motion vector caused by the mobile robot motion. The validity of the proposed algorithm has been verified by comparing the proposed algorithm in terms of map-building accuracy to conventional ICP algorithm real experiments.

Keywords

References

  1. S.-Y. Jung, "SLAM based on extended Kalman filter using laser range finder for mobile robot," M.s. thesis, Pusan National University, Feb. 2010.
  2. T. Tsubouch, "Nowadays trends in map generation for mobile robot," IEEE International conference on Intelligents Robots and Systems, vol. 2, pp. 828-833, 1996.
  3. J. Borenstein and L. Feng, "Measurement and correction of systematic odometry errors in mobile robots," IEEE Trans on Robotics and Automation, vol. 12, no. 6, pp. 869-880, 1996. https://doi.org/10.1109/70.544770
  4. K. Lee, C. Chung, and W. Chung, "Accurate calibration of kinematic parameters for two wheel differential mobile robots," Journal of Mechanical Science and Technology, vol. 25, no. 6, pp. 1603-1611, 2011. https://doi.org/10.1007/s12206-011-0334-y
  5. Y.-K. Kwon, "A path generation method for a autonomous mobile robot based on a virtual elastic force," The Journal of The Korea Institute of Electronic Communication Sciences, vol. 8, no. 1, pp. 149-157, 2013. https://doi.org/10.13067/JKIECS.2013.8.1.149
  6. Y.-S. Moon, S.-H. Roh, K.-H. Jo, and Y.-C. Bae, "Robot localization and monitoring using OpenRTM in outdoor environment based on precision GPS," The Journal of The Korea Institute of Electronic Communication Sciences, vol. 7, no. 2, pp. 425-431, 2012. https://doi.org/10.13067/JKIECS.2012.7.2.425
  7. T.-B. Kwon, J.-B. Song, and S.-C. Kang, "Extraction and matching of elevation moment of inertia for elevation mapbased localization of an outdoor mobile robot," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 15, no. 2, pp. 203-210, 2009. https://doi.org/10.5302/J.ICROS.2009.15.2.203
  8. K.-S. Yoon, "Improved localization algorithm for ultrasonic satellite system," The Journal of the Korea Institute of Electronic Communication Sciences, vol. 6, no. 5, pp. 775-781, 2011.
  9. C. Ye and J. Borenstein, "A new terrain mapping method for mobile robots obstacle negotiation," Processing of UGV Technology Conference, SPIE AeroSense Symposium, pp. 52-62, 2003.
  10. J.-S. Kim, K.-H. Ahn, and C.-H. Sung, "Refinements of multisensor based 3D reconstruction using a multi-sensor fusion disparity map," Journal of Korea Robotics Society, vol. 4, no. 4, pp. 298-304, 2008.
  11. S. Lee, Y. Kim, and H. Bang, "Experimental verification of multi-sensor geolocation algorithm using sequential Kalman filter," Journal of Institute of Control, Robotics and Systems, vol. 21, no. 1, pp. 7-13, 2015. https://doi.org/10.5302/J.ICROS.2015.14.9053
  12. D. Oritin, J. Neira, and J. M. M. Montiel, "Relocation using laser and vision," IEEE international conference on Robotis and Automation, vol. 2, pp. 1505-1510, 2004.
  13. S.-H. Kim, C.-W. Jho, and H.-K. Hong, "automatic registration method for multiple 3D range data sets," Journal of KISS : Software and Application, vol. 30, no. 11,12, pp. 1239-1246, 2003.
  14. S. Y. Cho, "Biaxial accelerometer-based magnetic compass module calibration and analysis of azimuth computational errors caused by accelerometer errors," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 20, no. 2, pp. 149-156, 2014. https://doi.org/10.5302/J.ICROS.2014.13.9008
  15. T. Fuiita and Y. Kondo, "3D terrain measurement system with movable laser range finder," 2009 IEEE International workshop on (SSRR), no. 2, pp. 1-6, Nov. 2009.
  16. Kazunori Ohno and Satoshi Tadokoro, "Dense 3D map building based on LRF data and color image fusion," IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2792-2797, 2005.
  17. H. Surmann, K. Lingemann, A. Nuchter, and J. Hertzberg, "A 3D laser range for autonomous mobile robots," Proc. of the 32nd ISR, pp. 153-158, 19-21, Apr. 2001.
  18. D. Oritin, J. Neira, and J. M. M. Montiel, "Relocation using laser and vision," IEEE international conference on Robotics and Automation, vol. 2, pp. 1505-1510, 2004.
  19. Y.-S. Hwang, H.-W. Kim, T.-J. Kim, and J.-M. Lee, "Impulse noise removal of LRF for 3D map building using a hybrid median filter," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 18, no. 10, pp. 970-976, 2012. https://doi.org/10.5302/J.ICROS.2012.18.10.970
  20. S.-W. Noh, T.-G. Kim, and N.-Y. Ko, "Map building using ICP algorithm based a robot position prediction," The Journal of The Korea Institute of Electronic Communication Sciences, vol. 8, no. 4, pp. 575-582, 2013. https://doi.org/10.13067/JKIECS.2013.8.4.575
  21. P. Besl and N. McKay, "A method for registration of 3-D shapes," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 2, pp. 239-256, 1992. https://doi.org/10.1109/34.121791
  22. S.-W. Noh, T.-G. Kim, and N.-Y. Ko, "Map building using ICP algorithm based a robot position prediction," The Journal of The Korea Institute of Electronic Communication Sciences, vol. 8, no. 4, pp. 575-582, 2013. https://doi.org/10.13067/JKIECS.2013.8.4.575
  23. A. Lorusso, D. Eggert, and R. Fisher, "A comparison of four algorithms for estimating 3-D rigid transformations," Proc. of the 4th British Machine Vision Conference (BMVC 1995), pp. 237-246, Sep. 1995.
  24. K. S. Arun, T. S. Huang, and S. D. Blostein, "Least square fitting of two 3-d point sets," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 9, no. 5, pp. 698-700, 1987.