Loop Closure Detection for Visual SLAM Based on Deep Learning

Hu, Hang; Zhang, Yunzhou; Duan, Qiang; Hu, Meiyu; Pang, Linzhuo

doi:10.1109/cyber.2017.8446473

Cited by 5 publications

(1 citation statement)

References 14 publications

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“…With deep learning becoming a popular research direction in recent years, researchers have gradually applied deep learning to visual SLAM. Deep learning methods were initially applied in land-based visual SLAM, where researchers applied deep learning methods to target recognition [76,77] and loop closure detection [78][79][80][81][82], achieving high-precision image recognition. From 2018 onwards, researchers began applying deep learning visual SLAM to the localization and navigation of underwater structures.…”

Section: Visual Slam Localizationmentioning

confidence: 99%

A Review of Underwater Robot Localization in Confined Spaces

Wu,

Chen,

Yang

et al. 2024

JMSE

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Underwater robots often encounter the influence of confined underwater environments during underwater exploration. These environments include underwater caves, sunken ships, submerged houses, and pipeline structures. Robot positioning in these environments is strongly disturbed, leading not only to the failure of some commonly used positioning methods but also to an increase in errors in positioning systems that normally function well in open water. In order to overcome the limitations of positioning methods in confined underwater environments, researchers have studied different underwater positioning methods and have selected suitable methods for positioning in such environments. These methods can achieve high-precision positioning without relying on assistance from other platforms and are referred to as autonomous positioning methods. Autonomous positioning methods for underwater robots mainly include SINS/DR positioning and SLAM positioning. In addition, in recent years, researchers have developed some bio-inspired autonomous positioning methods. This article introduces applicable robot positioning methods and sensors in confined underwater environments and discusses the research directions of robot positioning methods in such environments.

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