Self-supervised feature learning for rapid processing of marine imagery. Fast and reliable quantitative estimates of marine environmental health are needed for scientific studies, design and management of protected areas, and regulatory compliance of industrial activity in the ocean. Australia is collecting seafloor images at increasing rates but expert annotations are not keeping up, meaning that typical machine learning approaches struggle. This project will develop self-supervised techniques ....Self-supervised feature learning for rapid processing of marine imagery. Fast and reliable quantitative estimates of marine environmental health are needed for scientific studies, design and management of protected areas, and regulatory compliance of industrial activity in the ocean. Australia is collecting seafloor images at increasing rates but expert annotations are not keeping up, meaning that typical machine learning approaches struggle. This project will develop self-supervised techniques that use large amounts of unlabeled data to enhance performance. Our design takes advantage of additional information available for marine imagery such as geolocation and remote sensing context. We will explore how these representations can guide additional sampling and improve performance in classification tasks.Read moreRead less
Visual methods for advanced automation of underwater manipulation. This project will increase the autonomy of underwater robotic systems engaged in intervention and inspection tasks. Such activities are essential for the operation of subsea robotic systems used in offshore industries, scientific exploration and defence. Our approach will improve perception and situational awareness through the principled fusion of multiple navigation and camera sensors. We will use this improved scene understand ....Visual methods for advanced automation of underwater manipulation. This project will increase the autonomy of underwater robotic systems engaged in intervention and inspection tasks. Such activities are essential for the operation of subsea robotic systems used in offshore industries, scientific exploration and defence. Our approach will improve perception and situational awareness through the principled fusion of multiple navigation and camera sensors. We will use this improved scene understanding to effectively plan the motion of vehicles and manipulators through larger and more complex workspaces, enabling semi-supervised and autonomous task execution. Our project will demonstrate these capabilities in real-world deployments relevant to industry and marine science.Read moreRead less