<scp>NetHALOC</scp>: A learned global image descriptor for loop closing in underwater visual <scp>SLAM</scp>

Bonin‐Font, Francisco; Burguera, Antoni

doi:10.1111/exsy.12635

Cited by 18 publications

(5 citation statements)

References 40 publications

(69 reference statements)

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“…Bonin et al [92] presented an experimental evaluation of a hash-based loop-closure detection method for underwater autonomous vehicles utilizing a new global image de-scriptor called network-hash-based loop closure (NetHALOC). The conversion from images to hashes resulted in significantly fewer data to process, share, compare, and transfer in global navigation, localization, or mapping tasks.…”

Section: Loop-closure Detectionmentioning

confidence: 99%

An Overview of Key SLAM Technologies for Underwater Scenes

et al. 2023

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Autonomous localization and navigation, as an essential research area in robotics, has a broad scope of applications in various scenarios. To widen the utilization environment and augment domain expertise, simultaneous localization and mapping (SLAM) in underwater environments has recently become a popular topic for researchers. This paper examines the key SLAM technologies for underwater vehicles and provides an in-depth discussion on the research background, existing methods, challenges, application domains, and future trends of underwater SLAM. It is not only a comprehensive literature review on underwater SLAM, but also a systematic introduction to the theoretical framework of underwater SLAM. The aim of this paper is to assist researchers in gaining a better understanding of the system structure and development status of underwater SLAM, and to provide a feasible approach to tackle the underwater SLAM problem.

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Section: Loop-closure Detectionmentioning

confidence: 99%

An Overview of Key SLAM Technologies for Underwater Scenes

et al. 2023

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“…For instance, enabling AUVs and charging stations to communicate freely over the DAV network promotes commercial success, eliminating the barriers [81]. Blockchain Technology in AUV Design: Table 4 shows a comparative analysis of recent studies over UAV [83]- [90]. The comparative analysis shows that very few studies and proposals are available for blockchain-based autonomous vehicle systems.…”

Section: A Contemporary Research and Directions In Auvmentioning

confidence: 99%

Blockchain and Autonomous Vehicles: Recent Advances and Future Directions

et al. 2021

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Blockchain is an underlying technology for securing many real-time applications and their data. The automobile is one such sector in which auto-manufacturers are looking forward to accepting the advantages of distributed ledger technology in autonomous vehicles or systems and improving their products, customer satisfaction, and other valuable experiences. This work aims to find the significance of blockchain technology in Autonomous Vehicles, including Autonomous Electric Vehicles (AEV), Autonomous Underwater Vehicles (AUV), Autonomous Guided Vehicles (AGV), Autonomous Aerial Vehicles (AAeV), and Autonomous Driving. In this work, a comparative analysis of blockchain-integrated autonomous vehicle systems is explored to identify the present scenario and futuristic challenges. In addition to blockchain technology, the uses and importance of sensors, architectures and infrastructure requirements, vehicle types, driving modes, vehicles target and tracking approaches, intelligent contracts, intelligent data handling, and industry-specific use cases are also explored. This study is based on the exploration of recent technologies and practices. As autonomous vehicles are expected to be the future of intelligent transportation, this paper surveys recent advances in autonomous vehicles and systems and how blockchain can help in improving user experiences and improving industry practices. Finally, limitations of work, future research directions, and challenges associated with different autonomous vehicles and systems are presented.

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“…Zaffar et al [11] propose image areas of focus based on entropy measures and describe the image by regional descriptors which are convolutionally matched for viewpointinvariance. Bonin-Font and Burguera [12] present an approach in which each image in the training dataset has loop closured candidates synthesised from automatic random rotation over the image plane, scaling and shifting.…”

Section: Related Workmentioning

confidence: 99%

Contrastive Learning for Unsupervised Radar Place Recognition

Gadd

Martini

Newman

2021

2021 20th International Conference on Advanced Robotics (ICAR)

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We learn, in an unsupervised way, an embedding from sequences of radar images that is suitable for solving the place recognition problem with complex radar data. Our method is based on invariant instance feature learning but is tailored for the task of re-localisation by exploiting for data augmentation the temporal successivity of data as collected by a mobile platform moving through the scene smoothly. We experiment across two prominent urban radar datasets totalling over 400 km of driving and show that we achieve a new radar place recognition stateof-the-art. Specifically, the proposed system proves correct for 98.38 % of the queries that it is presented with over a challenging re-localisation sequence, using only the single nearest neighbour in the learned metric space. We also find that our learned model shows better understanding of out-of-lane loop closures at arbitrary orientation than non-learned radar scan descriptors.

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NetHALOC: A learned global image descriptor for loop closing in underwater visual SLAM

Cited by 18 publications

References 40 publications

An Overview of Key SLAM Technologies for Underwater Scenes

An Overview of Key SLAM Technologies for Underwater Scenes

Blockchain and Autonomous Vehicles: Recent Advances and Future Directions

Contrastive Learning for Unsupervised Radar Place Recognition

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