Monocular Visual Odometry with Unmanned Underwater Vehicle Using Low Cost Sensors

Dabove, Paolo; Pietra, Vincenzo Di; Piras, Marco

doi:10.1109/plans46316.2020.9109841

Cited by 6 publications

(4 citation statements)

References 29 publications

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“…The experiment’s results revealed that the method had a better performance compared with FastSLAM in underwater environments. Dabove et al presented a low-cost monocular visual odometry algorithm for underwater positioning [ 14 ]; the simulation experiments showed that this method could acquire effective and reliable results. An improved underwater SLAM algorithm, based on ORB features, was proposed by Lin et al [ 15 ], and the nonlinear optimization method was utilized to optimize the scale of visual odometry and the AUV pose.…”

Section: Related Work In Underwater Visual Positioningmentioning

confidence: 99%

An Underwater Positioning System for UUVs Based on LiDAR Camera and Inertial Measurement Unit

Yang

Jia

2022

Sensors

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Underwater positioning presents a challenging issue, because of the rapid attenuation of electronic magnetic waves, the disturbances and uncertainties in the environment. Conventional methods usually employed acoustic devices to localize Unmanned Underwater Vehicles (UUVs), which suffer from a slow refresh rate, low resolution, and are susceptible to the environmental noise. In addition, the complex terrain can also degrade the accuracy of the acoustic navigation systems. The applications of underwater positioning methods based on visual sensors are prevented by difficulties of acquiring the depth maps due to the sparse features, the changing illumination condition, and the scattering phenomenon. In the paper, a novel visual-based underwater positioning system is proposed based on a Light Detection and Ranging (LiDAR) camera and an inertial measurement unit. The LiDAR camera, benefiting from the laser scanning techniques, could simultaneously generate the associated depth maps. The inertial sensor would offer information about its altitudes. Through the fusion of the data from multiple sensors, the positions of the UUVs can be predicted. After that, the Bundle Adjustment (BA) method is used to recalculate the rotation matrix and the translation vector to improve the accuracy. The experiments are carried out in a tank to illustrate the effects and accuracy of the investigated method, in which the ultra-wideband (UWB) positioning system is used to provide reference trajectories. It is concluded that the developed positioning system is able to estimate the trajectory of UUVs accurately, whilst being stable and robust.

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Section: Related Work In Underwater Visual Positioningmentioning

confidence: 99%

An Underwater Positioning System for UUVs Based on LiDAR Camera and Inertial Measurement Unit

Yang

Jia

2022

Sensors

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“…However, such an algorithm requires a large volume of training data and it is not robust. Dabove [41] reported a monocular visual odometry using low cost sensors. Their method is based on the Kalman filter.…”

Section: Recent Work Of Visual Odometry and Underwater Navigationmentioning

confidence: 99%

An Integrated Visual Odometry System With Stereo Camera for Unmanned Underwater Vehicles

Haroutunian

Murphy

et al. 2022

IEEE Access

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Navigation is a challenging problem in the area of underwater unmanned vehicles, due to the significant electronmagnetic wave attenuation and the uncertainties in underwater environments. The conventional methods, mainly implemented by acoustic devices, suffer limitations such as high cost, terrain effects and low refresh rate. In this paper, a novel low-cost underwater visual navigation method, named Integrated Visual Odometry with a Stereo Camera (IVO-S), has been investigated. Unlike pure visual odometry, the proposed method fuses the information from inertial sensors and a sonar so that it is able to work in context-sparse environments. In practical experiments, the vehicle was operated to follow specific closed-loop shapes. The Integrated Visual Odoemtry with Monocular Camera (IVO-M) method and other popular open source Visual SLAMs (Simultaneous Localisation and Mappings), such as ORB-SLAM2 and VINS-Mono, have been used to provide comparative results. The cumulative error ratio is used as the quantitative evaluation method to analyse the practical test results. It is shown that the IVO-S method is able to work in underwater sparse-feature environments with high accuracy, whilst also being a low cost solution. INDEX TERMSUnderwater navigation, underwater vehicles, visual-inertial odometry, sensor fusion. sonar imaging, and wireless sensor networks at Newcastle University, in 2007, where he is currently a Senior Lecturer of communications and signal processing with the School of Electrical and Electronic Engineering. He has published over 100 conference and journal publications and his work on underwater acoustic communication and positioning has been commercialised by three U.K. companies. His research interests include underwater acoustic signal processing and device design, wireless communication networks, and biomedical instrumentation. ROSE NORMAN (Senior Member, IEEE) received the B.Eng. degree in electrical and electronic engineering from Leeds University, U.K., in 1989, and the M.Sc. and Ph.D. degrees in electrical engineering from Bradford University, U.K., in 1990 and 1994, respectively. She was the Principle Engineer at Switched Reluctance Drives Ltd., in 2004, where she is currently a Senior Lecturer with the School of Engineering. Her research interests include underwater vehicles, marine robotics and automation, and marine applications of data analytics and machine learning.

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“…Furthermore, we evaluate the three enhancement techniques on an open source underwater Aqualoc dataset [7], [8] for completeness. The Aqualoc dataset has been deployed only in [31] where it was used to validate a positioning solution based on monocular visual odometry. To the best of our knowledge this work is the first attempt to enhance arthroscopic images with such techniques for the purpose of frames registration.…”

Section: Surgical Visionmentioning

confidence: 99%

Saliency Improvement in Feature-Poor Surgical Environments Using Local Laplacian of Specified Histograms

et al. 2020

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Navigation in endoscopic environments requires an accurate and robust localisation system. A key challenge in such environments is the paucity of visual features that hinders accurate tracking. This paper examines the performance of three image enhancement techniques for tracking under such feature-poor conditions including Contrast Limited Adaptive Histogram Specification (CLAHS), Fast Local Laplacian Filtering (LLAP) and a new combination of the two coined Local Laplacian of Specified Histograms (LLSH). Two cadaveric knee arthroscopic datasets and an underwater seabed inspection dataset are used for the analysis, where results are interpreted by defining visual saliency as the number of correctly matched key-point (SIFT and SURF) features. Experimental results show a significant improvement in contrast quality and feature matching performance when image enhancement techniques are used. Results also demonstrate the LLSHs ability to vastly improve SURF tracking performance indicating more than 87% of successfully matched frames. A comparative analysis provides some important insights useful in the design of vision-based navigation for autonomous agents in feature-poor environments.

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Monocular Visual Odometry with Unmanned Underwater Vehicle Using Low Cost Sensors

Cited by 6 publications

References 29 publications

An Underwater Positioning System for UUVs Based on LiDAR Camera and Inertial Measurement Unit

An Underwater Positioning System for UUVs Based on LiDAR Camera and Inertial Measurement Unit

An Integrated Visual Odometry System With Stereo Camera for Unmanned Underwater Vehicles

Saliency Improvement in Feature-Poor Surgical Environments Using Local Laplacian of Specified Histograms

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