Underwater Visual Acoustic SLAM with Extrinsic Calibration

Shida Xu; Tomasz Luczynski; Jonatan Scharff Willners; Ziyang Hong; Kaicheng Zhang; Yvan R. Petillot; Sen Wang

doi:10.1109/IROS51168.2021.9636258

Underwater Visual Acoustic SLAM with Extrinsic Calibration

Shida Xu, Tomasz Luczynski, Jonatan Scharff Willners, Ziyang Hong, Kaicheng Zhang, Yvan R. Petillot, Sen Wang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

17 Citations (Scopus)

Abstract

Underwater scenarios are challenging for visual Simultaneous Localization and Mapping (SLAM) due to limited visibility and intermittently losing structures in image views. In this paper, we propose a visual acoustic bundle adjustment system which fuses a camera and a Doppler Velocity Log (DVL) in a graph SLAM framework for reliable underwater localization and mapping. In order to fuse the vision with the acoustic measurements, an calibration algorithm is also designed to estimate extrinsic parameters between a camera and a DVL using features detected in scenes. Experimental results in a tank and an offshore wind farm show the proposed method can achieve better robustness and localization accuracy than pure visual SLAM, especially in visually challenging scenarios, and the extrinsic calibration parameters can be accurately estimated, even when initialized with a random guess.

Original language	English
Title of host publication	IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Publisher	IEEE
Pages	7647-7652
Number of pages	6
ISBN (Electronic)	9781665417143
DOIs	https://doi.org/10.1109/IROS51168.2021.9636258
Publication status	Published - 16 Dec 2021
Event	2021 IEEE/RSJ International Conference on Intelligent Robots and Systems - Prague, Czech Republic Duration: 27 Sept 2021 → 1 Oct 2021

Conference

Conference	2021 IEEE/RSJ International Conference on Intelligent Robots and Systems
Abbreviated title	IROS 2021
Country/Territory	Czech Republic
City	Prague
Period	27/09/21 → 1/10/21

ASJC Scopus subject areas

Control and Systems Engineering
Software
Computer Vision and Pattern Recognition
Computer Science Applications

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10.1109/IROS51168.2021.9636258

Cite this

@inproceedings{971a2ee4d6474b90bc536cb56bf2ff1c,

title = "Underwater Visual Acoustic SLAM with Extrinsic Calibration",

abstract = "Underwater scenarios are challenging for visual Simultaneous Localization and Mapping (SLAM) due to limited visibility and intermittently losing structures in image views. In this paper, we propose a visual acoustic bundle adjustment system which fuses a camera and a Doppler Velocity Log (DVL) in a graph SLAM framework for reliable underwater localization and mapping. In order to fuse the vision with the acoustic measurements, an calibration algorithm is also designed to estimate extrinsic parameters between a camera and a DVL using features detected in scenes. Experimental results in a tank and an offshore wind farm show the proposed method can achieve better robustness and localization accuracy than pure visual SLAM, especially in visually challenging scenarios, and the extrinsic calibration parameters can be accurately estimated, even when initialized with a random guess. ",

author = "Shida Xu and Tomasz Luczynski and {Scharff Willners}, Jonatan and Ziyang Hong and Kaicheng Zhang and Petillot, {Yvan R.} and Sen Wang",

note = "Funding Information: ACKNOWLEDGMENT This work was supported by the EPSRC funded ORCA Hub (EP/R026173/1) and EU H2020 Programme under EU-MarineRobots project (grant ID 731103). The authors would like to thank Joshua Roe, Elizabeth Vargas, Roman Gabl and Leonard McLean for their technical support on data collection and trials. Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021 ; Conference date: 27-09-2021 Through 01-10-2021",

year = "2021",

month = dec,

day = "16",

doi = "10.1109/IROS51168.2021.9636258",

language = "English",

pages = "7647--7652",

booktitle = "IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)",

publisher = "IEEE",

address = "United States",

}

Xu, S, Luczynski, T, Scharff Willners, J, Hong, Z, Zhang, K, Petillot, YR & Wang, S 2021, Underwater Visual Acoustic SLAM with Extrinsic Calibration. in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, pp. 7647-7652, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, Prague, Czech Republic, 27/09/21. https://doi.org/10.1109/IROS51168.2021.9636258

TY - GEN

T1 - Underwater Visual Acoustic SLAM with Extrinsic Calibration

AU - Xu, Shida

AU - Luczynski, Tomasz

AU - Scharff Willners, Jonatan

AU - Hong, Ziyang

AU - Zhang, Kaicheng

AU - Petillot, Yvan R.

AU - Wang, Sen

N1 - Funding Information: ACKNOWLEDGMENT This work was supported by the EPSRC funded ORCA Hub (EP/R026173/1) and EU H2020 Programme under EU-MarineRobots project (grant ID 731103). The authors would like to thank Joshua Roe, Elizabeth Vargas, Roman Gabl and Leonard McLean for their technical support on data collection and trials. Publisher Copyright: © 2021 IEEE.

PY - 2021/12/16

Y1 - 2021/12/16

N2 - Underwater scenarios are challenging for visual Simultaneous Localization and Mapping (SLAM) due to limited visibility and intermittently losing structures in image views. In this paper, we propose a visual acoustic bundle adjustment system which fuses a camera and a Doppler Velocity Log (DVL) in a graph SLAM framework for reliable underwater localization and mapping. In order to fuse the vision with the acoustic measurements, an calibration algorithm is also designed to estimate extrinsic parameters between a camera and a DVL using features detected in scenes. Experimental results in a tank and an offshore wind farm show the proposed method can achieve better robustness and localization accuracy than pure visual SLAM, especially in visually challenging scenarios, and the extrinsic calibration parameters can be accurately estimated, even when initialized with a random guess.

AB - Underwater scenarios are challenging for visual Simultaneous Localization and Mapping (SLAM) due to limited visibility and intermittently losing structures in image views. In this paper, we propose a visual acoustic bundle adjustment system which fuses a camera and a Doppler Velocity Log (DVL) in a graph SLAM framework for reliable underwater localization and mapping. In order to fuse the vision with the acoustic measurements, an calibration algorithm is also designed to estimate extrinsic parameters between a camera and a DVL using features detected in scenes. Experimental results in a tank and an offshore wind farm show the proposed method can achieve better robustness and localization accuracy than pure visual SLAM, especially in visually challenging scenarios, and the extrinsic calibration parameters can be accurately estimated, even when initialized with a random guess.

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U2 - 10.1109/IROS51168.2021.9636258

DO - 10.1109/IROS51168.2021.9636258

M3 - Conference contribution

AN - SCOPUS:85124374162

SP - 7647

EP - 7652

BT - IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

PB - IEEE

T2 - 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems

Y2 - 27 September 2021 through 1 October 2021

ER -

Underwater Visual Acoustic SLAM with Extrinsic Calibration

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ASJC Scopus subject areas

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