mlsanityData to 7 Oct 2026

Hyperspectral 3D Mapping of Underwater Environments

arXiv 2110.06571 ↗First posted 13 Oct 2021cs.CV cs.ROcited by 1

Not on the map. Why?

Abstract (arXiv)

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 interpolating 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 hyperspectral 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 textures. 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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 5 of its 37 references that are in the map. First posted on arXiv 13 Oct 2021.

Builds on

5 of 37 references.

References · 5

  1. 2015ORB-SLAM: a Versatile and Accurate Monocular SLAM Systemcited by 983
  2. 2019AQUALOC: An Underwater Dataset for Visual-Inertial-Pressure Localizationcited by 18
  3. 2021OV2SLAM : A Fully Online and Versatile Visual SLAM for Real-Time Applicationscited by 8
  4. 2018Real-time Monocular Visual Odometry for Turbid and Dynamic Underwater Environmentscited by 6
  5. 2019Automatic Scale Estimation of Structure from Motion based 3D Models using Laser Scalerscited by 2

Built on by

Cited by year

Citing papers per year: 2023: 1.

1 paper.