Estimating vehicle ego-motion and piecewise planar scene structure from optical flow in a continuous framework
We propose a variational approach for estimating egomotion and structure of a static scene from a pair of images recorded by a single moving camera. In our approach the scene structure is described by a set of 3D planar surfaces, which are linked to a SLIC superpixel decomposition of the image domai...
Gespeichert in:
| Hauptverfasser: | , , , , |
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| Dokumenttyp: | Kapitel/Artikel |
| Sprache: | Englisch |
| Veröffentlicht: |
03 November 2015
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| In: |
Pattern Recognition
Year: 2015, Pages: 41-52 |
| DOI: | 10.1007/978-3-319-24947-6_4 |
| Online-Zugang: | Resolving-System, Volltext: http://dx.doi.org/10.1007/978-3-319-24947-6_4 Verlag, Volltext: https://link.springer.com/chapter/10.1007/978-3-319-24947-6_4 
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| Verfasserangaben: | Andreas Neufeld, Johannes Berger, Florian Becker, Frank Lenzen, Christoph Schnörr |
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| 520 | |a We propose a variational approach for estimating egomotion and structure of a static scene from a pair of images recorded by a single moving camera. In our approach the scene structure is described by a set of 3D planar surfaces, which are linked to a SLIC superpixel decomposition of the image domain. The continuously parametrized planes are determined along with the extrinsic camera parameters by jointly minimizing a non-convex smooth objective function, that comprises a data term based on the pre-calculated optical flow between the input images and suitable priors on the scene variables. Our experiments demonstrate that our approach estimates egomotion and scene structure with a high quality, that reaches the accuracy of state-of-the-art stereo methods, but relies on a single sensor that is more cost-efficient for autonomous systems. | ||
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