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Proceedings Paper

High-resolution hyperspectral ground mapping for robotic vision
Author(s): Frank Neuhaus; Christian Fuchs; Dietrich Paulus
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Paper Abstract

Recently released hyperspectral cameras use large, mosaiced filter patterns to capture different ranges of the light’s spectrum in each of the camera’s pixels. Spectral information is sparse, as it is not fully available in each location. We propose an online method that avoids explicit demosaicing of camera images by fusing raw, unprocessed, hyperspectral camera frames inside an ego-centric ground surface map. It is represented as a multilayer heightmap data structure, whose geometry is estimated by combining a visual odometry system with either dense 3D reconstruction or 3D laser data. We use a publicly available dataset to show that our approach is capable of constructing an accurate hyperspectral representation of the surface surrounding the vehicle. We show that in many cases our approach increases spatial resolution over a demosaicing approach, while providing the same amount of spectral information.

Paper Details

Date Published: 13 April 2018
PDF: 9 pages
Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106961K (13 April 2018); doi: 10.1117/12.2310066
Show Author Affiliations
Frank Neuhaus, Univ. of Koblenz-Landau (Germany)
Christian Fuchs, Univ. of Koblenz-Landau (Germany)
Dietrich Paulus, Univ. of Koblenz-Landau (Germany)


Published in SPIE Proceedings Vol. 10696:
Tenth International Conference on Machine Vision (ICMV 2017)
Antanas Verikas; Petia Radeva; Dmitry Nikolaev; Jianhong Zhou, Editor(s)

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