Abstract
In this paper, a scene perception and recognition module aimed at use in typical industrial scenarios is presented. The major contribution of this work lies in a 3D object detection, recognition and pose estimation module, which can be trained using CAD models and works for noisy data, partial views and in cluttered scenes. This algorithm was qualitatively and quantitatively compared with other state-of-art algorithms. Scene perception and recognition is an important aspect in the design of intelligent robotic systems which can adapt to unstructured and rapidly changing environments. This work has been used and evaluated in several experiments and demonstration scenarios for autonomous process plan execution, human-robot interaction and co-operation.
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References
Scovanner, P., Ali, S., Shah, M.: A 3-dimensional sift descriptor and its application to action recognition. In: Proceedings of the 15th International Conference on Multimedia, MULTIMEDIA 2007, pp. 357–360. ACM, New York (2007)
Sipiran, I., Bustos, B.: Harris 3d: a robust extension of the harris operator for interest point detection on 3d meshes. Vis. Comput. 27, 963–976 (2011)
Zhong, Y.: Intrinsic shape signatures: A shape descriptor for 3d object recognition. In: Computer Vision Workshops (ICCV Workshops), pp. 689–696 (2009)
Rusu, R.B., Bradski, G., Thibaux, R., Hsu, J.: Fast 3d recognition and pose using the viewpoint feature histogram. In: Intelligent Robots and Systems (IROS), pp. 2155–2162. IEEE (2010)
Rusu, R.B., Blodow, N., Beetz, M.: Fast point feature histograms (fpfh) for 3d registration. In: Robotics and Automation (ICRA), pp. 3212–3217. IEEE (2009)
Hu, G.: 3-d object matching in the hough space. In: IEEE International Conference on Systems, Man and Cybernetics, Intelligent Systems for the 21st Century, vol. 3, pp. 2718–2723 (1995)
Schnabel, R., Wessel, R., Wahl, R., Klein, R.: Shape recognition in 3d point-clouds. In: Skala, V. (ed.) The 16th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2008. UNION Agency-Science Press (2008)
Schnabel, R., Wahl, R., Klein, R.: Efficient ransac for point-cloud shape detection. Computer Graphics Forum 26, 214–226 (2007)
Papazov, C., Haddadin, S., Parusel, S., Krieger, K., Burschka, D.: Rigid 3D geometry matching for grasping of known objects in cluttered scenes. International Journal of Robotic Research 31, 538–553 (2012)
Papazov, C., Burschka, D.: An efficient ransac for 3d object recognition in noisy and occluded scenes. In: Proceedings of the 10th Asian Conference on Computer Vision - Volume Part I, pp. 135–148 (2011)
Hastie, T., Tibshirani, R., Friedman, J.: 14.3.12 Hierarchical clustering The Elements of Statistical Learning, 2nd edn. Springer, New York (2009) ISBN 0-387-84857-6
Gonzalez, R.C., Woods, R.: Digital Image Processing, 2nd edn. Prentice Hall, New Jersey (2002)
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 23, 1222–1239 (2001)
Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 1124–1137 (2004)
Delong, A., Osokin, A., Isack, H., Boykov, Y.: Fast approximate energy minimization with label costs. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2010), pp. 2173–2180 (2010)
Delong, A., Osokin, A., Isack, H.N., Boykov, Y.: Fast approximate energy minimization with label costs. Int. J. Comput. Vision 96, 1–27 (2012)
Leonardis, A., Gupta, A., Bajcsy, R.: Segmentation of range images as the search for geometric parametric models. Int. J. Comput. Vision 14, 253–277 (1995)
Boros, E., Hammer, P.L.: Pseudo-boolean optimization. Discrete Appl. Math. 123, 155–225 (2002)
Rother, C., Kolmogorov, V., Lempitsky, V., Szummer, M.: Optimizing binary mrfs via extended roof duality. In: Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8 (2007)
Barequet, G., Har-Peled, S.: Efficiently approximating the minimum-volume bounding box of a point set in three dimensions. J. Algorithms 38, 91–109 (2001)
Richtsfeld, A., Morwald, T., Prankl, J., Zillich, M., Vincze, M.: Segmentation of unknown objects in indoor environments. In: Intelligent Robots and Systems (IROS 2012), pp. 4791–4796 (2012)
Fikes, R.E., Nilsson, N.J.: Strips: A new approach to the application of theorem proving to problem solving. Technical Report 43R, AI Center, SRI International, 333 Ravenswood Ave, Menlo Park, CA 94025, SRI Project 8259 (1971)
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Somani, N., Dean-León, E., Cai, C., Knoll, A. (2013). Scene Perception and Recognition in Industrial Environments for Human-Robot Interaction. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2013. Lecture Notes in Computer Science, vol 8033. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41914-0_37
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DOI: https://doi.org/10.1007/978-3-642-41914-0_37
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