Online Informative Sampling using Semantic Features in Underwater Environments
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
Cite
@article{arxiv.2402.03636,
title = {Online Informative Sampling using Semantic Features in Underwater Environments},
author = {Shrutika Vishal Thengane and Yu Xiang Tan and Marcel Bartholomeus Prasetyo and Malika Meghjani},
journal= {arXiv preprint arXiv:2402.03636},
year = {2024}
}
Comments
In proceeding of IEEE/MTS OCEANS, 2024