Toward rich geometric map for SLAM : online detection of planes in 2D LIDAR

• Autorzy: Berger C.
• Języki publikacji: EN

Abstrakty

EN

Rich geometric models of the environment are needed for robots to carry out their missions. However a robot operating in a large environment would require a compact representation. In this article, we present a method that relies on the idea that a plane appears as a line segment in a 2D scan, and that by tracking those lines frame after frame, it is possible to estimate the parameters of that plane. The method is divided in three steps: fitting line segments on the points of the 2D scan, tracking those line segments in consecutive scan and estimating the parameters with a graph based SLAM (Simultaneous Localisation And Mapping) algorithm.

Słowa kluczowe

EN

laser scanner; SLAM; lidar

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2013
• Tom: Vol. 7, No. 1
• Strony: 35--41
• Opis fizyczny: Bibliogr. 19 poz., rys.

Twórcy

autor

Berger C.

• Department of Computer and Information Science,University of Linköping, SE-581 83 LINKÖPING, Sweden,

Bibliografia

• [1] T. Bailey, H. Durrant-Whyte, “Simultaneous Localisation and Mapping (SLAM): Part II – State of the Art”, Robotics and Automation Magazine, September 2006.
• [2] C. Berger, “Perception of the environment geometry for autonomous navigation”, PhD thesis, University of Toulouse, 2009.
• [3] C. Berger, “Weak constraints network optimiser”. In: IEEE International Conference on Robotics and Automation, 2012.
• [4] M. Duckham, L. Kulik, M.Worboys, A. Galton, “Efficient generation of simple polygons for characterizing the shape of a set of points in the plane”, Pattern Recognition, vol. 41, no. 10, 2008, pp. 3224–3236.
• [5] H. Durrant-Whyte, T. Bailey, “Simultaneous Localisation and Mapping (SLAM): Part I – The Essential Algorithms”, Robotics and Automation Magazine, June 2006.
• [6] M. Hebel, U. Stilla, “Pre-classification of points and segmentation of urban objects by scan line analysis of airborne lidar data”. In: International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 37, 2008, pp. 105–110.
• [7] J.H. Joung, K.H. An, J.W. Kang, M.J. Chung, W. Yu, “3d environment reconstruction using modified color icp algorithm by fusion of a camera and a 3d laser range finder”. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009, pp. 3082–3088.
• [8] M. Kirscht, C. Rinke, “3d reconstruction of buildings and vegetation from synthetic aperture radar (sar) images”. In: IAPR Workshop on Machine Vision Applications, 1998, pp. 17–19.
• [9] M. Morgan, A. Habib, “Interpolation of lidar data and automatic building extraction”. In: American Society of Photogrammetry and Remote Sensing Conference, 2002.
• [10] V. Nguyen, S. Gächter, A. Martinelli, N. Tomatis, R. Siegwart, “A comparison of line extraction algorithms using 2d range data for indoor mobile robotics”, Autonomous Robots, vol. 23, no. 2, 2007, pp. 97–111.
• [11] S.T. Pfister, S.I. Roumeliotis, J.W. Burdick,“Weighted line fitting algorithms for mobile robot ,map building and efficient data representation”. In: IEEE International Conference on Robotics and Automation, 2003, pp. 14–19.
• [12] T. Rodriguez, P. Sturm, P. Gargallo, N. Guilbert, A. Heyden, J.M. Menendez, and J.I. Ronda, “Photorealistic 3d reconstruction from handheld cameras”, Machine Vision and Applications, vol. 16, no. 4, 2005,pp. 246–257.
• [13] F. Rottensteiner, J. Jansa, “Automatic extraction of ,buildings from lidar data and aerial images”. In: International Society for Photogrammetry and Remote Sensing Symposium, 2002.
• [14] R.B. Rusu, S. Cousins, “3D is here: Point Cloud Library (PCL)”. In: IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, May 9–13, 2011.
• [15] M. Smith, I. Baldwin, W. Churchill, R. Paul, P. Newman, “The new college vision and laser data set”, The International Journal of Robotics Research, vol. 28, no. 5, May 2009, pp. 595–599.
• [16] G. Vosselman, S. Dijkman, “3d building model reconstruction from point clouds and ground plans”. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 34, pp. 37–44.
• [17] K.M. Wurm, A. Hornung, M. Bennewitz, C. Stachniss, W. Burgard, “Octomap: A probabilistic, flexible, and compact 3d map representation for robotic systems”. In: ICRA Workshop, 2010.
• [18] S.You, J. Hu,U.Neumann, P. Fox, “Urban site modeling from lidar”. In: International Conference on Computational Science and its Applications, ICCSA’03, Springer-Verlag, Berlin, Heidelberg 2003, pp. 579–588.
• [19] Z. Zhang, “Iterative point matching for registration of free-form curves and surfaces”, International Journal of Computer Vision, vol. 13, 1994, pp. 119–152.