Andreas Koch, Adam Berthelot, Bernd Eckstein, Oliver's Autonome Mobile Systeme 2009: 21. Fachgespräch Karlsruhe, PDF

By Andreas Koch, Adam Berthelot, Bernd Eckstein, Oliver Zweigle, Kai Häussermann (auth.), Rüdiger Dillmann, Jürgen Beyerer, Christoph Stiller, J. Marius Zöllner, Tobias Gindele (eds.)

ISBN-10: 3642102832

ISBN-13: 9783642102837

ISBN-10: 3642102840

ISBN-13: 9783642102844

Das 21. Fachgespräch 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 für den Gedankenaustausch bietet und wissenschaftliche Diskussionen sowie Kooperationen auf diesem Forschungsgebiet fördert bzw. initiiert. Inhaltlich finden sich ausgewählte Beiträge zu den Themen Humanoide Roboter und Flugmaschinen, Perzeption und Sensorik, Kartierung und Lokalisation, Regelung, Navigation, Lernverfahren, Systemarchitekturen sowie der Anwendung von autonomen mobilen Systemen.

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With its 360 degree field of view, only the LIDAR is able to sense the right branch, whereas vision might fail sometimes. This is because of the limited field of view of the visual sensor and the fact that if there was no tentacle selected leading into the branch there will be no gaze control for visually focussing the branch. Thus, MuCAR-3 will go straight on and miss the branch even if non-visible tentacles are not rated as completely non drivable (see Sec. 2). Nevertheless there are by far more advantages then disadvantages using the described fusion process.

Object recognition frameworks using such features usually operate on a set of computed feature correspondences, either by simply counting feature correspondences or by also exploiting the spatial relationships of the feature points, as proposed in [10]. Operating on the 2D localization result of such a framework, the common approach for 6-DoF pose estimation of objects computes the rotation and translation of the object in 3D space on the basis of 2D-3D point correspondences. The traditional method for this is the POSIT algorithm [7].

4. A. Wedel, U. Franke, J. Klappstein, T. Brox, and D. Cremers. Realtime Depth Estimation and Obstacle Detection from Monocular Video. DAGM, pages 475–484, 2006. 5. E. Einhorn, Ch. -J. -M. Gross. A Hybrid Kalman Filter Based Algorithm for Real-time Visual Obstacle Detection. In ECMR, pages 156–161, 2007. 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.

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Autonome Mobile Systeme 2009: 21. Fachgespräch Karlsruhe, 3./4. Dezember 2009 by Andreas Koch, Adam Berthelot, Bernd Eckstein, Oliver Zweigle, Kai Häussermann (auth.), Rüdiger Dillmann, Jürgen Beyerer, Christoph Stiller, J. Marius Zöllner, Tobias Gindele (eds.)


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