Abstract
Vision systems for service robotics applications have to cope with varying environmental conditions, partial occlusions, complex backgrounds and a large number of distractors (clutter) present in the scene. This paper presents a new approach targeted at such application scenarios that combines segmentation, object recognition, 3D localization and tracking in a seamlessly integrated fashion. The unifying framework is the probabilistic representation of various aspects of the scene. Experiments indicate that this approach is viable and gives very satisfactory results.
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Wolfram Burgard, Dieter Fox, Daniel Henning, and Timo Schmidt. Estimating the absolute position of a mobile robot using position probability grids. Technical report, Universität Bonn, Institut für Informatik III, 1996.
Keinosuke Fukunaga. Introduction to Statistical Pattern Recognition. Computer Science and Scientific Computing. Academic Press, Inc., 2 edition, 1990.
Brian V. Funt and Graham D. Finlayson. Color constant color indexing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(5):522–529, 1995.
M. Isard and A. Blake. Condensation-conditional density propagation for visual tracking. Intern. Journal on Computer Vision, 29(1):5–28, 1998.
Hans P. Moravec. Robot spatial perception by stereoscopic vision and 3d evidence grids. Technical Report CMU-RI-TR-96-34, Carnegie Mellon University, Robotics Institute, Pittsburgh, USA, 1996.
Hans P. Moravec and Alberto Elfes. High resolution maps from wide angle sonar. In Intern. Conf. on Robotics and Automation, pages 19–24, 1985.
J. Peng and B. Bhanu. Closed-loop object recognition using reinforcement learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(2):139–154, 1998.
M. Reinhold, F. Deinzer, J. Denzler, D. Paulus, and J. Pösl. Active appearance-based object recognition using viewpoint selection. In B. Girod, G. Greiner, H. Niemann, and H.-P. Seidel, editors, Vision, Modeling, and Visualization 2000, pages 105–112. infix, Berlin, 2000.
Bernt Schiele and James Crowley. Probabilistic object recognition using multidimensional receptive field histograms. In Proc. of the Intern. Conf. on Pattern Recognition (ICPR’96), pages 50–54, 1996.
Bernt Schiele and James Crowley. Where to look next and what to look for. In Proc. of the Conf. on Intelligent Robots and Systems (IROS’96), pages 1249–1255, 1996.
Henry Schneiderman and Takeo Kanade. A statistical model for 3d object detection applied to faces and cars. In IEEE Conference on Computer Vision and Pattern Recognition. IEEE, June 2000.
M. J. Swain and D. H. Ballard. Color indexing. International Journal on Computer Vision, 7(1):11–32, 1991.
S. Thrun, D. Fox, and W. Burgard. Monte carlo localization with mixture proposal distribution. In Proc. of the Seventh National Conference on Artificial Intelligence (AAAI), 2000.
Sebastian B. Thrun. A baysian approach to landmark discovery and active perception in mobile robot navigation. Technical Report CMU-CS-96-122, School of Computer Science, Carnegie Mellon University, Pittsburgh, USA, 1996.
Georg von Wichert, Thomas Wösch, Steffen Gutmann, and Gisbert Lawitzky. MobMan-Ein mobiler Manipulator für Alltagsumgebungen. In R. Dillmann, H. Wörn, and M. von Ehr, editors, Autonome Mobile Systeme 2000, pages 55–62. Springer, 2000.
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von Wichert, G. (2001). A Probabilistic Approach to Simultaneous Segmentation, Object Recognition, 3D Localization, and Tracking Using Stereo. In: Radig, B., Florczyk, S. (eds) Pattern Recognition. DAGM 2001. Lecture Notes in Computer Science, vol 2191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45404-7_14
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DOI: https://doi.org/10.1007/3-540-45404-7_14
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