Deep Learning-Based Inertial Navigation Technology for Autonomous Underwater Vehicle Long-Distance Navigation—A Review

被引：4

作者：

QinYuan He ^{[1
]}

HuaPeng Yu ^{[1
]}

YuChen Fang ^{[2
]}

机构：

[1] National Innovation Institute of Defense Technology, Academy of Military Science, Beijing

[2] School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu

来源：

Gyroscopy and Navigation | 2023年 / 14卷 / 03期

关键词：

AUV; component; deep learning; inertial navigation; underwater localization;

D O I：

10.1134/S2075108723030070

中图分类号：

学科分类号：

摘要：

Abstract: Autonomous navigation technology is the key technology for Autonomous Underwater Vehicle (AUV) to achieve automated, intelligent operation and task processing. Inertial navigation technology is the core of autonomous navigation technology for AUV. Traditional inertial navigation technology has been developed for many years, and it is necessary to find new breakthroughs. Deep learning can automatically select and extract key features of input data, which has been widely used in image recognition, speech recognition, natural language processing and other fields, and has good results in processing sequential data such as text and speech. Inertial navigation data clearly belongs to this type of data, and many scholars in the industry have conducted related research and design, and found that deep neural network models can be used to calibrate the noise of inertial sensors, reduce the drift of inertial navigation mechanisms, and fuse inertial information with other sensor information, with good effects in solving the prediction and error suppression of inertial navigation during long-term underwater voyages. This article provides a comprehensive review of deep learning-based inertial navigation for AUV, including the latest research progress and development trend direction. © 2023, Pleiades Publishing, Ltd.

引用

页码：267 / 275

页数：8

共 56 条

[1]

Matos A., Cruz N., Development and implementation of a low-cost LBL navigation system for an AUV, Ocean ’99 MTS/IEEE, Riding the Crest into 21St Century, pp. 774-779

[2]

Opderbecke J.P., Person R., Rybicki F., POSIDONIA 6000: A new long range highly accurate ultra short base line positioning system, Ocean MTS/IEEE Conference Proceedings, pp. 1721-1727, (1998)

[3]

Thomas H.G., GIB buoys: An interface between space and depths of the oceans, Proceedings of the 1998 Workshop on Autonomous Underwater Vehicles, pp. 181-184

[4]

Watanabe Y., Ochi H., Et al., A tracking of AUV with integration of SSBL acoustic positioning and transmitted INS data, Proc. OCEAN EUROPE, pp. 1-6, (2009)

[5]

Toky A., Singh R., Das S., Localization schemes for underwater acoustic sensor networks — A review, Comput. Sci. Rev., 37, pp. 100241-100259, (2020)

[6]

Bresson G., Alsayed Z., Simultaneous localization and mapping: A survey of current trends in autonomous driving, IEEE Trans. Intell., 2, pp. 194-220, (2017)

[7]

Qin T., Li P., Shen S., VINS-Mono: A robust and versatile monocular visual-inertial state estimator, IEEE Transactions on Robotics (ITOR), no. 4, pp. 1004-1020, (2018)

[8]

Xu W., Cai Y., Et al., Fast-LIO2: Fast direct LiDAR-inertial odometry, IEEE Transactions on Robotics, 38, 4, (2022)

[9]

Feng D., Wang C., Kalman-filter based integration of IMU and UWB for high-accuracy indoor positioning and navigation, IEEE Internet of Things J., 7, pp. 3133-3146, (2020)

[10]

Wagner J., Sorg H.W., The Bohnenberger machine, Gyroscopy and Navigation, 1, pp. 73-78, (2010)

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