By Rudiger Dillmann, Jurgen Beyerer, Christoph Stiller, Marius Zollner, Tobias Gindele
Das 21. Fachgespr?ch Autonome cellular Systeme (AMS 2009) ist ein discussion board, das Wissenschaftlerinnen und Wissenschaftlern aus Forschung und Industrie, die auf dem Gebiet der autonomen mobilen Systeme arbeiten, eine foundation f?r den Gedankenaustausch bietet und wissenschaftliche Diskussionen sowie Kooperationen auf diesem Forschungsgebiet f?rdert bzw. initiiert. Inhaltlich finden sich ausgew?hlte Beitr?ge zu den Themen Humanoide Roboter und Flugmaschinen, Perzeption und Sensorik, Kartierung und Lokalisation, Regelung, Navigation, Lernverfahren, Systemarchitekturen sowie der Anwendung von autonomen mobilen Systemen.
Read or Download Autonome Mobile Systeme 2009 (21. Fachgesprach Karlsruhe, 3. 4. Dezember 2009) (Reihe: Informatik aktuell) PDF
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Additional resources for Autonome Mobile Systeme 2009 (21. Fachgesprach Karlsruhe, 3. 4. Dezember 2009) (Reihe: Informatik aktuell)
While this approach may work well in outdoor scenes, where the objects near the focus of expansion are separated from the background by large depth discontinuities, it will fail in cluttered indoor environments like home improvement stores or home environments. In  we propose an early version of a feature-based approach for monocular scene reconstruction. This shape-from-motion approach uses extended Kalman ﬁlters (EKF) to reconstruct the 3D position of the image features in real-time in order to identify potential obstacles in the reconstructed scene.
For this, usually a second order parabola is ﬁtted to the measured disparity/correlation pairs and the two neighbors. 5 pixels is achieved easily by this approach. Together with Eq. 6 cm. 44 P. Azad, T. Asfour and R. Dillmann Judging from the presented theoretical calculations, the position accuracy that can be achieved by stereo vision is higher by a factor of approx. 2–3. g. grasping the handle of a cup, the lower estimated accuracy of methods relying on 2D-3D correspondences is problematic, for many other applications it might be suﬃcient.
6. J. D. D. Molton, and O. Stasse. MonoSLAM: Real-Time Single Camera SLAM. IEEE Trans. on PAMI, 29(6):1052–1067, 2007. 7. J. J. Davison, and J. Montiel. Inverse Depth Parametrization for Monocular SLAM. IEEE Trans. on Robotics, 24(5):932–945, Oct. 2008. 8. E. Eade and T. Drummond. Monocular SLAM as a Graph of Coalesced Observations. In IEEE Int. Conference on Computer Vision, ICCV, pages 1–8, 2007. 9. E. Eade and T. Drummond. Uniﬁed Loop Closing and Recovery for Real Time Monocular SLAM. In Proc.