Hyperspectral 3D Mapping of Underwater Environments

Type Proceedings paper
Date 2021
Language English
Other localization https://iccv2021.thecvf.com/home
Author(s) Ferrera Maxime1, Arnaubec Aurelien1, Istenic Klemen2, Gracias Nuno2, Bajjouk Touria1
Affiliation(s) 1 : French Research Institute for Exploitation of the Sea (IFREMER), France
2 : Research Centre in Underwater Robotics (CIRS), University of Girona, Spain
Meeting ICCV 2021 - International Conference on Computer Vision. October 11 to October 17, Virtual
Source Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2021, pp. 3696-3705
DOI 10.1109/ICCVW54120.2021.00413
Abstract

Hyperspectral imaging has been increasingly used for underwater survey applications over the past years. As many hyperspectral cameras work as push-broom scanners, their use is usually limited to the creation of photo-mosaics based on a flat surface approximation and by interpolat- ing the camera pose from dead-reckoning navigation. Yet, because of drift in the navigation and the mostly wrong flat surface assumption, the quality of the obtained photo- mosaics is often too low to support adequate analysis. In this paper we present an initial method for creating hyper- spectral 3D reconstructions of underwater environments. By fusing the data gathered by a classical RGB camera, an inertial navigation system and a hyperspectral push- broom camera, we show that the proposed method creates highly accurate 3D reconstructions with hyperspectral tex- tures. We propose to combine techniques from simultaneous localization and mapping, structure-from-motion and 3D reconstruction and advantageously use them to create 3D models with hyperspectral texture, allowing us to overcome the flat surface assumption and the classical limitation of dead-reckoning navigation.

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How to cite 

Ferrera Maxime, Arnaubec Aurelien, Istenic Klemen, Gracias Nuno, Bajjouk Touria (2021). Hyperspectral 3D Mapping of Underwater Environments. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2021, pp. 3696-3705. https://archimer.ifremer.fr/doc/00728/83974/