Abstract
In this article we describe an algorithm for constructing a compact representation of 3D laser range data. Our approach extracts a dictionary of local scans from the scene. The words of this dictionary are used to replace recurrent local 3D structures, which leads to a substantial compression of the entire point cloud. We optimize our model in terms of complexity and accuracy by minimizing the Bayesian information criterion (BIC). Experimental evaluations on large real-world datasets show that the described method allows robots to accurately reconstruct environments with as few as 70 words. Furthermore the experiments suggest that the proposed representation gives a richer semantic description than pure occupancy based representations.
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References
Nelson, M.R.: LZW data compression. Dr. Dobb’s Journal 14(10), 36 (1989)
Ruhnke, M., Steder, B., Grisetti, G., Burgard, W.: Unsupervised learning of compact 3d models based on the detection of recurrent structures. In: Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Taipei, Taiwan (October 2010)
Steder, B., Rusu, R.B., Konolige, K., Burgard, W.: NARF: 3D range image features for object recognition. In: Workshop on Defining and Solving Realistic Perception Problems in Personal Robotics at the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), Taipei, Taiwan (2010), http://ais.informatik.uni-freiburg.de/publications/papers/steder10irosws.pdf
Dataset of 3600 3D scans of the Faculty of Engineering, University of Freiburg, Germany, http://ais.informatik.uni-freiburg.de/projects/datasets/fr360
Rusu, R.B., Bradski, G., Thibaux, R., Hsu, J.: Fast 3d recognition and pose using the viewpoint feature histogram. In: Proceedings of the 23rd IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Taipei, Taiwan (October 2010)
Johnson, A.E., Hebert, M.: Using spin images for efficient object recognition in cluttered 3d scenes. IEEE Trans. Pattern Anal. Mach. Intell. 21(5), 433–449 (1999)
Cummins, M., Newman, P.: Highly Scalable Appearance-Only SLAM FAB-MAP 2.0. In: Proc. of Robotics Science and Systems (RSS) (2009)
Nüchter, A., Lingemann, K., Hertzberg, J., Surmann, H.: 6d SLAM with approximate data association. In: Proc. of the 12th Int. Conference on Advanced Robotics (ICAR), pp. 242–249 (2005)
Besl, P.J., McKay, N.D.: A method for registration of 3-d shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(2), 239–256 (1992)
Fairfield, N., Kantor, G.A., Wettergreen, D.: Real-time slam with octree evidence grids for exploration in underwater tunnels. Journal of Field Robotics (2007)
Triebel, R., Pfaff, P., Burgard, W.: Multi-level surface maps for outdoor terrain mapping and loop closing. In: Proc. of the Int. Conf. on Intelligent Robots and Systems (IROS), Beijing, China (2006)
de la Puente, P., Rodriguez-Losada, D., Valero, A., Matia, F.: 3D Feature Based Mapping Towards Mobile Robots Enhanced Performance in Rescue Missions. In: Proc. IEEE International Conference on Intelligent Robots and Systems, IROS 2009 (2009)
Limketkai, B., Liao, L., Fox, D.: Relational object maps for mobile robots. In: International Joint Conference on Artificial Intelligence, Citeseer, vol. 19, p. 1471 (2005)
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© 2012 Springer-Verlag GmbH Berlin Heidelberg
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Ruhnke, M., Steder, B., Grisetti, G., Burgard, W. (2012). 3D Environment Modeling Based on Surface Primitives. In: Prassler, E., et al. Towards Service Robots for Everyday Environments. Springer Tracts in Advanced Robotics, vol 76. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25116-0_19
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DOI: https://doi.org/10.1007/978-3-642-25116-0_19
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