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 AUV s can generate a sig-nificant amount of data. In addition, sending live data feed from an un...

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Bibliographic Details
Published inOCEANS 2024 - Singapore pp. 1 - 6
Main Authors Thengane, Shrutika Vishal, Tan, Yu Xiang, Prasetyo, Marcel Bartholomeus, Meghjani, Malika
Format Conference Proceeding
LanguageEnglish
Published IEEE 15.04.2024
Subjects
Autonomous underwater vehicles
Measurement
Memory
Object detection
Oceans
Online Informative Sampling
Online Summa-rization
ROST
Semantic Features
Semantics
Underwater Exploration
Video Summarization
Visualization
Online AccessGet full text
DOI10.1109/OCEANS51537.2024.10682405

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Summary: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 AUV s can generate a sig-nificant amount of data. In addition, sending live data feed from an underwater environment requires dedicated on-board data storage options for AUV s 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.
DOI:10.1109/OCEANS51537.2024.10682405