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An Integrated Visual Odometry System for Underwater Vehicles

Lookup NU author(s): Zhizun Xu, Dr Maryam HaroutunianORCiD, Dr Alan J Murphy, Professor Jeffrey Neasham, Dr Rosemary NormanORCiD



This is the authors' accepted manuscript of an article that has been published in its final definitive form by IEEE, 2021.

For re-use rights please refer to the publisher's terms and conditions.


Underwater navigation is always a challenging problem, because of electromagnetic attenuation. The traditional methods involve beacons, inertial sensors, and Doppler Velocity Log (DVL), but they have many shortcomings, such as high cost, and lengthy setup time. In order to solve underwater navigation problems at low cost, an integrated visual odometry system has been developed and discussed in this paper. In this method, two inertial sensors provide acceleration and attitude of the vehicle, and an underwater sonar is used to provide the distance between the vehicle and the seabed, whilst in the visual odometry section, an optical flow algorithm has been applied for tracking feature points. With the depth provided by the sonar, 3D position of feature points can be calculated. Linear motion of the vehicle is then predicted through these feature points in dual frames. Finally, nonlinear optimization is used to correct the attitude of the vehicle using visual information. In the proposed algorithm, the vehicle trajectory can be estimated in absolute scale by using a single camera; computational complexity is reduced dramatically compared to other visual odometry methodologies; and this algorithm allows the approach to work in sparse texture conditions. The results from practical experiments demonstrate that the method is effective and it is also a low-cost solution.

Publication metadata

Author(s): Xu Z, Haroutunian M, Murphy AJ, Neasham J, Norman RA

Publication type: Article

Publication status: Published

Journal: IEEE Journal of Oceanic Engineering

Year: 2021

Volume: 46

Issue: 3

Pages: 848-863

Print publication date: 01/07/2021

Online publication date: 30/12/2020

Acceptance date: 27/10/2020

Date deposited: 09/11/2020

ISSN (print): 0364-9059

ISSN (electronic): 1558-1691

Publisher: IEEE


DOI: 10.1109/JOE.2020.3036710


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