Efficient Navigation Aware Seabed Coverage using AUVs

Özkahraman, Özer; Ögren, Petter

doi:10.1109/ssrr53300.2021.9597862

Cited by 5 publications

(3 citation statements)

References 20 publications

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“…As shown in our previous work [24], the rectangular coverage pattern, also known as a lawnmower pattern, is the most efficient way to do coverage as long as the area A is rectangular and has one side that is a multiple of the effective sensor width. In the wide-area coverage problems that we handle in this paper, both of these constraints are easy to satisfy.…”

Section: A Mirrored Lawnmower Pattern With Rendezvousmentioning

confidence: 95%

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Collaborative Navigation-Aware Coverage in Feature-Poor Environments

Özkahraman¹,

Ögren²

2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Multi agent coverage and robot navigation are two very important research fields within robotics. However, their intersection has received limited attention. In multi agent coverage, perfect navigation is often assumed, and in robot navigation, the focus is often to minimize the localization error with the aid of stationary features from the environment. The need for integration of the two becomes clear in environments with very sparse features or landmarks, for example when a group of Autonomous Underwater Vehicles (AUVs) are to search a uniform seafloor for mines or other dangerous objects. In such environments, localization systems are often deprived of detectable features to use that could increase their accuracy. In this paper we propose an algorithm for doing navigation aware multi agent coverage in areas with no landmarks. Instead of using identical lawn mower patterns, we propose to mirror every other pattern to enable the agents to meet up and make inter-agent measurements and share information regularly. This improves performance in two ways, global drift in relation to the area to be covered is reduced, and local coverage gaps between adjacent patterns are reduced. Further, we show that this can be accomplished within the constraints of very limited sensing, computing and communication resources that most AUVs have available. The effectiveness of our method is shown through statistically significant simulated experiments.

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Section: A Mirrored Lawnmower Pattern With Rendezvousmentioning

confidence: 95%

“…While these methods are useful on their own, they address the localization error by increasing overlap, severely limiting the maximum coverage area. An analysis of the reduced efficiency and maximum coverage areas for different types of coverage patterns have been done in [24].…”

Section: Related Workmentioning

confidence: 99%

Collaborative Navigation-Aware Coverage in Feature-Poor Environments

Özkahraman¹,

Ögren²

2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Self Cite

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“…Machine vision can work steadily for long periods with tiny cumulative time errors [17,18]. Thus, underwater visual SLAM has been developed and applied for location awareness of the underwater environment and assisted navigation in recent years, which has excellent potential for unmanned surveys and free cruising applications [19][20][21][22][23]. The application of underwater SLAM is widely studied.…”

Section: Introductionmentioning

confidence: 99%

An Underwater Localization Method Based on Visual SLAM for the Near-Bottom Environment

Liu,

Wang,

et al. 2024

JMSE

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The feature matching of the near-bottom visual SLAM is influenced by underwater raised sediments, resulting in tracking loss. In this paper, the novel visual SLAM system is proposed in the underwater raised sediments environment. The underwater images are firstly classified based on the color recognition method by adding the weights of pixel location to reduce the interference of similar colors on the seabed. The improved adaptive median filter method is proposed to filter the classified images by using the mean value of the filter window border as the discriminant condition to retain the original features of the image. The filtered images are finally processed by the tracking module to obtain the trajectory of underwater vehicles and the seafloor maps. The datasets of seamount areas captured in the western Pacific Ocean are processed by the improved visual SLAM system. The keyframes, mapping points, and feature point matching pairs extracted from the improved visual SLAM system are improved by 5.2%, 11.2%, and 4.5% compared with that of the ORB-SLAM3 system, respectively. The improved visual SLAM system has the advantage of robustness to dynamic disturbances, which is of practical application in underwater vehicles operated in near-bottom areas such as seamounts and nodules.

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