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arXiv · 2402.03636

Online Informative Sampling using Semantic Features in Underwater Environments

Shrutika Vishal Thengane·Yu Xiang Tan·Marcel Bartholomeus Prasetyo·Malika Meghjani
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Abstract

The underwater world remains largely unexplored, with Autonomous Underwater Vehicles (AUVs) playing a crucial role in sub-sea explorations. However, continuous monitoring of underwater environments using AUVs can generate a significant amount of data. In addition, sending live data feed from an underwater environment requires dedicated on-board data storage options for AUVs which can hinder requirements of other higher priority tasks. Informative sampling techniques offer a solution by condensing observations. In this paper, we present a semantically-aware online informative sampling (ON-IS) approach which samples an AUV's visual experience in real-time. Specifically, we obtain visual features from a fine-tuned object detection model to align the sampling outcomes with the desired semantic information. Our contributions are (a) a novel Semantic Online Informative Sampling (SON-IS) algorithm, (b) a user study to validate the proposed approach and (c) a novel evaluation metric to score our proposed algorithm with respect to the suggested samples by human subjects

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Shrutika Vishal Thengane, Yu Xiang Tan, Marcel Bartholomeus Prasetyo, Malika Meghjani. 2024-02-06. Online Informative Sampling using Semantic Features in Underwater Environments. https://arxiv.org/abs/2402.03636

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