AEKF-SLAM: A New Algorithm for Robotic Underwater Navigation

Yuan, Xin; Martínez-Ortega, José-Fernán; Fernández, José Antonio Sánchez; Eckert, Martina

doi:10.3390/s17051174

Cited by 34 publications

(13 citation statements)

References 46 publications

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“…Underwater navigation is the process of acquiring the position of an underwater platform in absolute or relative coordinates with respect to the environment [5]. Applications requiring underwater navigation include exploration of the deep ocean [6], naval surveillance [7], aquatic habitat surveys [8], dam, harbor and ship inspections, military actions [9], etc. However, autonomous underwater navigation is still an open problem.…”

Section: Introductionmentioning

confidence: 99%

Sonar FoV Segmentation for Motion Estimation Using DL Networks

2022

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Accurate navigation of an autonomous underwater vehicle is important for its reliable operation. However, this task is challenging due to limitations of radio wave propagation and poor visibility in the aquatic environment. Underwater navigation techniques based on analysing sonar images facilitated by machine learning have shown promising results. However, previously proposed techniques are still complicated for real-time applications. This paper investigates low complexity techniques for the motion estimation based on the use of images obtained by a sonar looking down to the seafloor. The sonar can use multiple beams within a field of view (FoV). Various configurations of beams are considered according to portions of the FoV covered and two estimation approaches are investigated. In one approach, the sonar images are directly processed by a deep learning (DL) network, whereas in the other, the images are converted into (reduced size) vectors before applying them to a DL network. The vector approach shows a significantly lower computation time (about 10 times faster), which makes it suitable for realtime applications. Both the approaches show a similar estimation accuracy, about 10% of the maximum magnitude of the motion. The vector technique has been used to estimate a simulated trajectory and compare the estimate with the ground truth, which showed a good match. It has also been applied to estimate the trajectory of an imaging sonar from a real data set from a ship's hull inspection. The estimated trajectory has successfully been used to build a mosaic by merging the sonar images from the real data set.

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Section: Introductionmentioning

confidence: 99%

Sonar FoV Segmentation for Motion Estimation Using DL Networks

2022

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“…Traditionally, researchers have focused on the use of local appearance methods, and it can be considered a mature technology to solve the mapping and localization problems. Many approaches are proposed in the literature based on these descriptors [ 24 , 25 , 26 , 27 , 28 ]. Typically, they require the implementation of detection, description and tracking algorithms which tend to be relatively complex and computationally expensive.…”

Section: Introductionmentioning

confidence: 99%

The Role of Global Appearance of Omnidirectional Images in Relative Distance and Orientation Retrieval

Román

Payá

Peidró

et al. 2021

Sensors

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Over the last few years, mobile robotics has experienced a great development thanks to the wide variety of problems that can be solved with this technology. An autonomous mobile robot must be able to operate in a priori unknown environments, planning its trajectory and navigating to the required target points. With this aim, it is crucial solving the mapping and localization problems with accuracy and acceptable computational cost. The use of omnidirectional vision systems has emerged as a robust choice thanks to the big quantity of information they can extract from the environment. The images must be processed to obtain relevant information that permits solving robustly the mapping and localization problems. The classical frameworks to address this problem are based on the extraction, description and tracking of local features or landmarks. However, more recently, a new family of methods has emerged as a robust alternative in mobile robotics. It consists of describing each image as a whole, what leads to conceptually simpler algorithms. While methods based on local features have been extensively studied and compared in the literature, those based on global appearance still merit a deep study to uncover their performance. In this work, a comparative evaluation of six global-appearance description techniques in localization tasks is carried out, both in terms of accuracy and computational cost. Some sets of images captured in a real environment are used with this aim, including some typical phenomena such as changes in lighting conditions, visual aliasing, partial occlusions and noise.

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“…In the darkness of deep waters, the fauna is also a cause of visual degradation as animals are attracted by the artificial light and tend to get in the field of view of the camera, leading to dynamism and occlusions in the images. In front of these difficulties, many works tackle the underwater localization problem using sonar systems [11,12,13], as they do not suffer from these visual degradation. Nevertheless, the information delivered by a sonar is not as rich as optical images [14] and remains very challenging to analyze.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Monocular Visual Odometry for Turbid and Dynamic Underwater Environments

Ferrera

Moras

Trouvé-Peloux

et al. 2019

Sensors

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In the context of underwater robotics, the visual degradation induced by the medium properties make difficult the exclusive use of cameras for localization purpose. Hence, many underwater localization methods are based on expensive navigation sensors associated with acoustic positioning. On the other hand, pure visual localization methods have shown great potential in underwater localization but the challenging conditions, such as the presence of turbidity and dynamism, remain complex to tackle. In this paper, we propose a new visual odometry method designed to be robust to these visual perturbations. The proposed algorithm has been assessed on both simulated and real underwater datasets and outperforms state-of-the-art terrestrial visual SLAM methods under many of the most challenging conditions. The main application of this work is the localization of Remotely Operated Vehicles used for underwater archaeological missions, but the developed system can be used in any other applications as long as visual information is available.

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AEKF-SLAM: A New Algorithm for Robotic Underwater Navigation

Cited by 34 publications

References 46 publications

Sonar FoV Segmentation for Motion Estimation Using DL Networks

Sonar FoV Segmentation for Motion Estimation Using DL Networks

The Role of Global Appearance of Omnidirectional Images in Relative Distance and Orientation Retrieval

Real-Time Monocular Visual Odometry for Turbid and Dynamic Underwater Environments

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