Abstract
Multi-view stereo cameras and RGB-D cameras are widely used in robotic vision for 3D map reconstruction in navigation tasks. RGB-D cameras provide accurate depth measurements even in textureless areas, but are sensitive to distortion of its actively projected patterns. Stereo cameras are reliable if and only if there are sufficient features in the visible region. The two kinds of sensors are complementary in performance, so we combine them to a three-view RGB-D system and propose a fusion method for reliable 3D point cloud reconstruction. Furthermore, the reliability of the reconstructed map is vital for robotic navigation, so we build a spatial uncertainty model for the system, which can be easily specialized to either subsystems. The fusion method is shown to have gain in performance from the spatial uncertainty perspectives.
This work has been performed in the framework of the project VaMEx (Valles Marineris Explorer) which is partly funded by the Federal Ministry of Economics and Technology administered by DLR Space Agency (FKZ 50NA1213) and the Bavarian Ministry of Economic Affairs, Infrastructure, Transport and Technology administered by IABG GmbH (ZB 20-8-3410.2-12-2012).
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Zhu, C., Bilgeri, S., Günther, C. (2014). Spatial Uncertainty Model of a Three-View RGB-D Camera System. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8888. Springer, Cham. https://doi.org/10.1007/978-3-319-14364-4_12
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DOI: https://doi.org/10.1007/978-3-319-14364-4_12
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14363-7
Online ISBN: 978-3-319-14364-4
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