Underwater Visual Localization Using Machine Learning and LSTM: Experiments, and References

Authors: Luyuan Peng, Acoustic Research Laboratory, National University of Singapore; Hari Vishnu, Acoustic Research Laboratory, National University of Singapore; Mandar Chitre, Acoustic Research Laboratory, National University of Singapore; Yuen Min Too, Acoustic Research Laboratory, National University of Singapore; Bharath Kalyan, Acoustic Research Laboratory, National University of Singapore; Rajat Mishra, Acoustic Research Laboratory, National University of Singapore.

This research project is supported by A*STAR under its RIE2020 Advanced Manufacturing and Engineering Industry Alignment Fund - Pre-Positioning Grant No. A20H8a0241. We test three model configurations on both the simulator and tank datasets. First, we implement a pose regression model based on GoogLeNet as our baseline model. Secondly, we implement a model using ResNet-50 to evaluate the improvement possible by use of a deeper network. ResNet allows us to have deeper networks with more free parameters, and tries to avoid the problem of overfitting by having residual connections between layers .

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Underwater Visual Localization Using Machine Learning and LSTM: IntroductionThis paper explores using machine learning and LSTM for visual localization in underwater environments, achieving accurate positioning with underwater datasets.
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Underwater Visual Localization Using Machine Learning and LSTM: MethodThis paper explores using machine learning and LSTM for visual localization in underwater environments, achieving accurate positioning with underwater datasets.
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Underwater Visual Localization Using Machine Learning and LSTM: DatasetsThis paper explores using machine learning and LSTM for visual localization in underwater environments, achieving accurate positioning with underwater datasets.
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