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

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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3D Simultaneous Localization and Map Building (SLAM) using a 2D Laser Range Finder based on Vertical/Horizontal Planar Polygons

2차원 레이저 거리계를 이용한 수직/수평 다각평면 기반의 위치인식 및 3차원 지도제작

  • Lee, Seungeun (Department of Electrical Engineering, KAIST (Korea Advanced Institute of Science and Technology)) ;
  • Kim, Byung-Kook (Department of Electrical Engineering, KAIST (Korea Advanced Institute of Science and Technology))
  • 이승은 (한국과학기술원 전기 및 전자공학과) ;
  • 김병국 (한국과학기술원 전기 및 전자공학과)
  • Received : 2014.03.14
  • Accepted : 2014.07.21
  • Published : 2014.11.01
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Abstract

An efficient 3D SLAM (Simultaneous Localization and Map Building) method is developed for urban building environments using a tilted 2D LRF (Laser Range Finder), in which a 3D map is composed of perpendicular/horizontal planar polygons. While the mobile robot is moving, from the LRF scan distance data in each scan period, line segments on the scan plane are successively extracted. We propose an "expected line segment" concept for matching: to add each of these scan line segments to the most suitable line segment group for each perpendicular/horizontal planar polygon in the 3D map. After performing 2D localization to determine the pose of the mobile robot, we construct updated perpendicular/horizontal infinite planes and then determine their boundaries to obtain the perpendicular/horizontal planar polygons which constitute our 3D map. Finally, the proposed SLAM algorithm is validated via extensive simulations and experiments.

Keywords

References

  1. S. Thrun, W. Burgard, and D. Fox, "A real-time algorithm for mobile robot mapping with applications to multi-robot and 3D mapping," IEEE International Conference on Robotics and Automation, vol. 1, pp. 321-328, 2000.
  2. P. Kohlhepp, P. Pozzo, M. Walther, and R. Dillmann "Sequential 3D-SLAM for mobile action planning," IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 1, pp. 722-729, Sep. 2004.
  3. J. H. Jung, "Real-time indoor localization and 3d obstacle detection using laser range finder," M.S. Thesis, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea, p. 53, 2003.
  4. P. J. Besl and N. D. McKay, "A method for registration of 3D shapes," in Robotics-DL tentative, International Society for Optics and Photonics, pp. 586-606, 1992.
  5. L. Oswald, "Recent development of the Iterative Closet Point algorithm. An overview of the years 2008 to 2010," Autonomous Systems Lab, p. 27, 2010.
  6. M. Magnusson, A. Lilienthal, and T. Duckett, "Scan registration for autonomous mining vehicles using 3D-NDT," Journal of Field Robotics, vol. 24, no. 10, pp. 803-827, 2007. https://doi.org/10.1002/rob.20204
  7. J. Weingarten, "Feature-based 3D SLAM," Ph.D. Thesis, Swiss Federal Institute of Technology in Lausanne, p. 125, 2006.
  8. K. Pathak, N. Vaskevicius, J. Poppinga, M. Pfingsthorn, S. Schwertfeger, and A. Birk, "Fast 3D mapping by matching planes extracted from range sensor point-clouds," IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1150-1155, Oct. 2009.
  9. D. M. Cole and P. M. Newman, "Using laser range data for 3D SLAM in outdoor environments," IEEE International Conference on Robotics and Automation, pp. 1556-1563, 2006.
  10. Y. S. Hwang, J. H. Han, H. W. Kim, and J. M. Lee, "A 3D map building algorithm for a mobile robot moving on the slanted surface," Journal of Control, Robotics and systems (in Korean), vol. 18, no. 3, pp. 243-250, Mar. 2012. https://doi.org/10.5302/J.ICROS.2012.18.3.243
  11. B. Huhle, T. Schairer, S. Herholz, A. Schilling, and W. Strasser, "Sparse Registration-3D Reconstruction from Pairs of 2D Line Scans," Wilhelm-Schickard-Institut fur Informatik at the University of Tubingen, Germany, p. 13, 2013.
  12. B. K. Kim, J. B. Kim, and G. W. Yoon, "Method and apparatus for bidirectional line and arc segmentation of scan distance data for localization of mobile robots," KOREA Patent, Application Number 10-2012-0005646, 2012.
  13. H. J. Sohn, "Vector-based localiztation and map building for mobile robots using laser range finders in an indoor environment," Ph.D. Thesis, Korea Advanced Institue of Science and Technology, Daejeon, Republic of Korea, p. 169, 2008.
  14. J. Erickson, http://compgeom.cs.uiuc.edu/-jeffe/teaching/373/notes/x06-sweepline.pdf, lecture notes.
  15. J. B. Kim, G. W. Yoon, and B. K. Kim, "Development of laser range finder simulator based on 2.5D map for mobile robot localization in urban outdoor," Proc. of 2012 27th ICROS Annual Conference, Seoul, Republic of Korea, pp. 198-199, 2012.
  16. W. C. Lim, "Development of a cost-effective safety system with curb detection and avoidance for electric scooters," MS. Thesis, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea, p. 72, 2010.

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