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In this paper, we propose a novel method for autonomously seeking out sparsely distributed targets in an unknown underwater environment. Our Sparse Adaptive Search and Sample (SASS) algorithm mixes low-altitude observations of discrete targets with high-altitude observations of the surrounding substrates. By using prior information about the distribution of targets across substrate types in combination with belief modelling over these substrates in the environment, high-altitude observations provide information that allows SASS to quickly guide the robot to areas with high target densities. A maximally informative path is autonomously constructed online using Monte Carlo Tree Search with a novel acquisition function to guide the search to maximise observations of unique targets. We demonstrate our approach in a set of simulated trials using a novel generative species model. SASS consistently outperforms the canonical boustrophedon planner by up to 36% in seeking out unique targets in the first 75 - 90% of time it takes for a boustrophedon survey. Additionally, we verify the performance of SASS on two real world coral reef datasets. 

Phytoplankton communities in the open ocean are high‐dimensional, sparse, and spatiotemporally heterogeneous. The advent of automated imaging systems has enabled high‐resolution observation of these communities, but the amounts of data and their statistical properties make analysis with traditional approaches challenging. Spatiotemporal topic models offer an unsupervised and interpretable approach to dimensionality reduction of sparse, high‐dimensional categorical data. Here we use topic modeling to analyze neural‐network‐classified phytoplankton imagery taken in and around a retentive eddy during the 2021 North Atlantic EXport Processes in the Ocean from Remote Sensing (EXPORTS) field campaign. We investigate the role physical‐biological interactions play in altering plankton community composition within the eddy. Analysis of a water mass mixing framework suggests that storm‐driven surface advection and stirring were major drivers of the progression of the eddy plankton community away from a diatom bloom over the course of the cruise. 

Real-time computer vision and remote visual sensing platforms are increasingly used in numerous underwater applications such as shipwreck mapping, subsea inspection, coastal water monitoring, surveillance, coral reef surveying, invasive fish tracking, and more. Recent advancements in robot vision and powerful single-board computers have paved the way for an imminent revolution in the next generation of subsea technologies. In this chapter, we present these exciting emerging applications and discuss relevant open problems and practical considerations. First, we delineate the specific environmental and operational challenges of underwater vision and highlight some prominent scientific and engineering solutions to ensure robust visual perception. We specifically focus on the characteristics of underwater light propagation from the perspective of image formation and photometry. We also discuss the recent developments and trends in underwater imaging literature to facilitate the restoration, enhancement, and filtering of inherently noisy visual data. Subsequently, we demonstrate how these ideas are extended and deployed in the perception pipelines of Autonomous Underwater Vehicles (AUVs) and Remotely Operated Vehicles (ROVs). In particular, we present several use cases for marine life monitoring and conservation, human-robot cooperative missions for inspecting submarine cables and archaeological sites, subsea structure or cave mapping, aquaculture, and marine ecology. We elaborately discuss how the deep visual learning and on-device AI breakthroughs are transforming the perception, planning, localization, and navigation capabilities of visually-guided underwater robots. Along this line, we also highlight the prospective future research directions and open problems at the intersection of computer vision and underwater robotics domains. 

Abstract In-situ visual observations of marine organisms is crucial to developing behavioural understandings and their relations to their surrounding ecosystem. Typically, these observations are collected via divers, tags, and remotely-operated or human-piloted vehicles. Recently, however, autonomous underwater vehicles equipped with cameras and embedded computers with GPU capabilities are being developed for a variety of applications, and in particular, can be used to supplement these existing data collection mechanisms where human operation or tags are more difficult. Existing approaches have focused on using fully-supervised tracking methods, but labelled data for many underwater species are severely lacking. Semi-supervised trackers may offer alternative tracking solutions because they require less data than fully-supervised counterparts. However, because there are not existing realistic underwater tracking datasets, the performance of semi-supervised tracking algorithms in the marine domain is not well understood. To better evaluate their performance and utility, in this paper we provide (1) a novel dataset specific to marine animals located athttp://warp.whoi.edu/vmat/, (2) an evaluation of state-of-the-art semi-supervised algorithms in the context of underwater animal tracking, and (3) an evaluation of real-world performance through demonstrations using a semi-supervised algorithm on-board an autonomous underwater vehicle to track marine animals in the wild.