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.
To train and test our model, we used one dataset collected from an underwater robotics simulator as shown in Fig. 3 as well as two datasets collected from a tank as shown in Fig. 2. In the simulator dataset, we operated the ROV in simulation to perform inspection on a vertical pipe in a spiral motion. The total spatial extent covered by the ROV during the inspection is about 2×4×2 m. 14,400 samples of image-pose pair data were collected.
To train and test our model, we used one dataset collected from an underwater robotics simulator as shown in Fig. 3 as well as two datasets collected from a tank as shown in Fig. 2. In the simulator dataset, we operated the ROV in simulation to perform inspection on a vertical pipe in a spiral motion. The total spatial extent covered by the ROV during the inspection is about 2×4×2 m. 14,400 samples of image-pose pair data were collected.
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