This paper explores using machine learning and LSTM for visual localization in underwater environments, achieving accurate positioning with underwater datasets.
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.
We implement two different architectures. The first uses pretrained deep convolutional neural networks to extract features from the input images. The resulting feature map is then passed to an affine regressor consisting of dense neural layers to output a 7-dimensional pose vector estimates. The second architecture adds an additional LSTM layer between the DCNN and affine regressor to perform dimensionality reduction by assessing the spatial structure of the image.
We implement two different architectures. The first uses pretrained deep convolutional neural networks to extract features from the input images. The resulting feature map is then passed to an affine regressor consisting of dense neural layers to output a 7-dimensional pose vector estimates. The second architecture adds an additional LSTM layer between the DCNN and affine regressor to perform dimensionality reduction by assessing the spatial structure of the image.
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