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

翻译：水下世界在很大程度上仍未被探索，自主水下航行器（AUV）在海底探索中发挥着关键作用。然而，利用AUV对水下环境进行连续监测会产生大量数据。此外，从水下环境发送实时数据流需要AUV配备专用板载数据存储选项，这可能会妨碍其他更高优先级任务的需求。信息采样技术通过压缩观测数据提供了一种解决方案。本文提出了一种语义感知的在线信息采样（ON-IS）方法，该方法能够实时采样AUV的视觉体验。具体而言，我们从微调后的目标检测模型中提取视觉特征，使采样结果与所需的语义信息对齐。我们的贡献包括：（a）一种新颖的语义在线信息采样（SON-IS）算法，（b）一项用于验证所提方法的用户研究，以及（c）一种新的评估指标，用于根据人类受试者建议的样本对本文算法进行评分。