Monocular ORB-SLAM application in underwater scenarios

Franco Hidalgo; Chris Kahlefendt; Thomas Braunl

doi:10.1109/OCEANSKOBE.2018.8559435

Monocular ORB-SLAM application in underwater scenarios

Franco Hidalgo, Chris Kahlefendt, Thomas Braunl

School of Engineering

Research output: Chapter in Book/Conference paper › Conference paper

1 Citation (Scopus)

Abstract

This paper presents an experimental evaluation of monocular ORB-SLAM applied to underwater scenarios. It is investigated as an alternative SLAM method with minimal instu-mentation compared to other approaches that integrate different sensors such as inertial and acoustic sensors. ORB-SLAM creates a 3D map based on image frames and estimates the position of the robot by using a feature-based front-end and a graph-based back-end. The performance of ORB-SLAM is evaluated through experiments in different settings with varying lighting, visibility and water dynamics. Results show good performance given the right conditions and demonstrate that ORB-SLAM can work well in the underwater environment. Based on our findings the paper outlines possible enhancements which should further improve on the algorithms performance.

Original language	English
Title of host publication	2018 OCEANS - MTS/IEEE Kobe Techno-Oceans, OCEANS - Kobe 2018
Publisher	IEEE, Institute of Electrical and Electronics Engineers
ISBN (Electronic)	9781538616543
DOIs	https://doi.org/10.1109/OCEANSKOBE.2018.8559435
Publication status	Published - 4 Dec 2018
Event	2018 OCEANS - MTS/IEEE Kobe Techno-Oceans, OCEANS - Kobe 2018 - Kobe, Japan Duration: 28 May 2018 → 31 May 2018

Conference

Conference	2018 OCEANS - MTS/IEEE Kobe Techno-Oceans, OCEANS - Kobe 2018
Country	Japan
City	Kobe
Period	28/05/18 → 31/05/18

Access to Document

10.1109/OCEANSKOBE.2018.8559435

Link to publication in Scopus

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Cite this

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title = "Monocular ORB-SLAM application in underwater scenarios",

abstract = "This paper presents an experimental evaluation of monocular ORB-SLAM applied to underwater scenarios. It is investigated as an alternative SLAM method with minimal instu-mentation compared to other approaches that integrate different sensors such as inertial and acoustic sensors. ORB-SLAM creates a 3D map based on image frames and estimates the position of the robot by using a feature-based front-end and a graph-based back-end. The performance of ORB-SLAM is evaluated through experiments in different settings with varying lighting, visibility and water dynamics. Results show good performance given the right conditions and demonstrate that ORB-SLAM can work well in the underwater environment. Based on our findings the paper outlines possible enhancements which should further improve on the algorithms performance.",

author = "Franco Hidalgo and Chris Kahlefendt and Thomas Braunl",

year = "2018",

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day = "4",

doi = "10.1109/OCEANSKOBE.2018.8559435",

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publisher = "IEEE, Institute of Electrical and Electronics Engineers",

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AU - Hidalgo, Franco

AU - Kahlefendt, Chris

AU - Braunl, Thomas

PY - 2018/12/4

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N2 - This paper presents an experimental evaluation of monocular ORB-SLAM applied to underwater scenarios. It is investigated as an alternative SLAM method with minimal instu-mentation compared to other approaches that integrate different sensors such as inertial and acoustic sensors. ORB-SLAM creates a 3D map based on image frames and estimates the position of the robot by using a feature-based front-end and a graph-based back-end. The performance of ORB-SLAM is evaluated through experiments in different settings with varying lighting, visibility and water dynamics. Results show good performance given the right conditions and demonstrate that ORB-SLAM can work well in the underwater environment. Based on our findings the paper outlines possible enhancements which should further improve on the algorithms performance.

AB - This paper presents an experimental evaluation of monocular ORB-SLAM applied to underwater scenarios. It is investigated as an alternative SLAM method with minimal instu-mentation compared to other approaches that integrate different sensors such as inertial and acoustic sensors. ORB-SLAM creates a 3D map based on image frames and estimates the position of the robot by using a feature-based front-end and a graph-based back-end. The performance of ORB-SLAM is evaluated through experiments in different settings with varying lighting, visibility and water dynamics. Results show good performance given the right conditions and demonstrate that ORB-SLAM can work well in the underwater environment. Based on our findings the paper outlines possible enhancements which should further improve on the algorithms performance.

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