Online Informative Sampling using Semantic Features in Underwater Environments
<|reference_start|>Online Informative Sampling using Semantic Features in Underwater Environments: 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<|reference_end|>
@article{thengane2024online,
    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},
    archivePrefix={arXiv},
    eprint={2402.03636},
    primaryClass={cs.RO}
}