Abstract
A main task for domestic robots is to navigate safely at home, find places and detect objects. We set out to exploit the knowledge available to the robot to constrain the task of understanding the structure of its environment, i.e., ground for safe motion and walls for localisation, to simplify object detection and classification. We start from exploiting the known geometry and kinematics of the robot to obtain ground point disparities. This considerably improves robustness in combination with a histogram approach over patches in the disparity image. We then show that stereo data can be used for localisation and eventually for object detection classification and that this system approach improves object detection and classification rates considerably.
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References
Arras, K., Castellanos, J., Schilt, M., Siegwart, R.: Feature-based multi-hypothesis localization and tracking using geometric constraints. Robotics and Autonomous Systems 1(44), 41–53 (2003)
Einramhof, P., Vincze, M.: Stereo-based real-time scene segmentation for a home robot. In: International Symposium ELMAR (2010)
Elinas, P., Little, J.: omcl: Monte-carlo localization for mobile robots with stereo vision. In: Proceedings of Robotics: Science and Systems, Cambridge, MA, USA, pp. 373–380 (2005)
Golovinskiy, A., Kim, V.G., Funkhouser, T.: Shape-based recognition of 3d point clouds in urban environments. In: ICCV (2009)
Helmer, S., Lowe, D.: Using stereo for object recognition. In: ICRA (2010)
Humenberger, C., Zinner, C., Weber, M., Kubinger, W., Vincze, M.: A fast stereo matching algorithm suitable for embedded real-time systems. Computer Vision and Image Understanding 114, 1180–1202 (2010)
Johnson, A.E., Hebert, M.: Using spin images for efficient object recognition in cluttered 3d scenes. IEEE Transactions on Pattern Analysis and Machine Intelligences 21(5), 433–449 (1999)
Kazhdan, M., Funkhouser, T., Rusinkiewicz, S.: Rotation invariant spherical harmonic representation of 3d shape descriptors. In: SGP, pp. 156–164 (2003)
Lai, K., Fox, D.: Object detection in 3d point clouds using web data and domain adaptation. International Journal of Robotics Research (2010)
Meger, D., Gupta, A., Little, J.: Viewpoint detection models for sequential embodied object category recognition. In: 2010 IEEE International Conference on Robotics and Automation (ICRA), pp. 5055–5061 (2010), doi:10.1109/ROBOT.2010.5509703
Olufs, S., Vincze, M.: An efficient area-based observation model for monte-carlo robot localization. In: International Conference on Intelligent Robots and Systems IROS 2009, St. Louis, USA (2009)
Pfeifer, R., Lungarella, M., Iida, F.: Self-organization, embodiment, and biologically inspired robotics. Science 318, 1088–1093 (2007)
Plagemann, C., Kersting, K., Pfaff, P., Burgard, W.: Gaussian beam processes: A nonparametric bayesian measurement model for range finders. In: Robotics: Science and Systems (RSS), Atlanta, Georgia, USA (2007)
Pylyshyn, Z.: Visual indexes, preconceptual objects, and situated vision. Cognition 80, 127–158 (2001)
Rusu, R.B., Blodow, N., Marton, Z.C., Beetz, M.: Close-range scene segmentation and reconstruction of 3d point cloud maps for mobile manipulation in domestic environments. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (2009)
Schwarz, R., Olufs, S., Vincze, M.: Merging line segments in 3d using mean shift algorithm in man-made environment. Austrian Association for Pattern Recognition (2010)
Swadzba, A., Wachsmuth, S.: Indoor scene classification using combined 3d and gist features. In: Asian Conference on Computer Vision, Queenstown, New Zealand, vol. 2, pp. 725–739 (2010)
Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics, 1st edn. MIT Press, Cambridge (2005)
Thrun, S., Fox, D., Burgard, W.: A real-time algorithm for mobile robot mapping with application to multi robot and 3d mapping. In: International Conference on Robotics & Automation, San Francisco, CA, USA (2000)
Thrun, S., Fox, D., Burgard, W., Dellaert, F.: Robust monte carlo localization for mobile robots. Artificial Intelligence 128(1-2), 99–141 (2000)
Varadarajan, K., Vincze, M.: 3d room modeling and doorway detection from indoor stereo imagery using feature guided piecewise depth diffusion. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (2010)
Viswanathan, P., Meger, D., Southey, T., Little, J.J., Mackworth, A.: Automated spatial-semantic modeling with applications to place labeling and informed search. In: CRV (2009)
Wohlkinger, W., Vincze, M.: 3d object classification for mobile robots in home-environments using web-data. In: IEEE International Workshop on Robotics in Alpe-Adria-Danube Region RAAD (2010)
Wohlkinger, W., Vincze, M.: Shape-based depth image to 3d model matching and classification with inter-view similarity. Submitted to IEEE IROS (2011)
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Vincze, M., Wohlkinger, W., Olufs, S., Einramhof, P., Schwarz, R., Varadarajan, K. (2012). Object Detection and Classification for Domestic Robots. In: Hähnle, R., Knoop, J., Margaria, T., Schreiner, D., Steffen, B. (eds) Leveraging Applications of Formal Methods, Verification, and Validation. ISoLA 2011. Communications in Computer and Information Science, vol 336. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34781-8_8
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DOI: https://doi.org/10.1007/978-3-642-34781-8_8
Publisher Name: Springer, Berlin, Heidelberg
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