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Robust AUV Visual Loop Closure Detection Based on Variational Auto-Encoder Network

Wang, Yangyang and Ma, Xiaorui and Wang, Jie and Hou, Shilong and Dai, Ju and Gu, Dongbing and Wang, Hongyu (2022) 'Robust AUV Visual Loop Closure Detection Based on Variational Auto-Encoder Network.' IEEE Transactions on Industrial Informatics. p. 1. ISSN 1551-3203

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The visual loop closure detection for Autonomous Underwater Vehicles (AUVs) is a key component to reduce the drift error accumulated in simultaneous localization and mapping tasks. However, due to viewpoint changes, textureless images, and fast-moving objects, the loop closure detection in dramatically changing underwater environments remains a challenging problem to traditional geometric methods. Inspired by strong feature learning ability of deep neural networks, we propose an underwater loop closure detection method based on a variational auto-encoder network in this paper. Our proposed method can learn effective image representations to overcome the challenges caused by dynamic underwater environments. Specifically, the proposed network is an unsupervised method, which avoids the difficulty and cost of labeling a great quantity of underwater data. Also included is a semantic object segmentation module, which is utilized to segment the underwater environments and assign weights to objects in order to alleviate the impact of fast-moving objects. Furthermore, an underwater image description scheme is used to enable efficient access to geometric and object-level semantic information, which helps to build a robust and real-time system in dramatically changing underwater scenarios. Finally, we test the proposed system under complex underwater environments and get a recall rate of 92.31% in the tested environments.

Item Type: Article
Uncontrolled Keywords: AUV SLAM; loop closure detection; semantic segmentation; deep neural network
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 11 Feb 2022 21:04
Last Modified: 11 Feb 2022 21:04

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