Long-range Marine Autonomous Systems (MAS), operating beyond the visual line-of-sight of a human pilot or research ship, are creating unprecedented opportunities for oceanographic data collection. Able to operate for up to months at a time, periodically communicating with a remote pilot via satellite, long-range MAS vehicles significantly reduce the need for an expensive research ship presence within the operating area. Heterogeneous fleets of MAS vehicles, operating simultaneously in an area for an extended period of time, are becoming increasingly popular due to their ability to provide an improved composite picture of the marine environment. However, at present, the expansion of the size and complexity of these multi-vehicle operations is limited by a number of factors: (1) custom control-interfaces require pilots to be trained in the use of each individual vehicle, with limited cross-platform standardization; (2) the data produced by each vehicle are typically in a custom vehicle-specific format, making the automated ingestion of observational data for near-real-time analysis and assimilation into operational ocean models very difficult; (3) the majority of MAS vehicles do not provide machine-to-machine interfaces, limiting the development and usage of common piloting tools, multi-vehicle operating strategies, autonomous control algorithms and automated data delivery. In this paper, we describe a novel piloting and data management system (C2) which provides a unified web-based infrastructure for the operation of long-range MAS vehicles within the UK's National Marine Equipment Pool. The system automates the archiving, standardization and delivery of near-real-time science data and associated metadata from the vehicles to end-users and Global Data Assembly Centers mid-mission. Through the use and promotion of standard data formats and machine interfaces throughout the C2 system, we seek to enable future opportunities to collaborate with both the marine science and robotics communities to maximize the delivery of high-quality oceanographic data for world-leading science.
Shadows have long been a challenging topic for computer vision. This challenge is made even harder when we assume that the camera is moving, as many existing shadow detection techniques require the creation and maintenance of a background model. This article explores the problem of shadow modelling from a moving viewpoint (assumed to be a robotic platform) through comparing shadow-variant and shadowinvariant image featuresprimarily color, texture and edgebased features. These features are then embedded in a segmentation pipeline that provides predictions on shadow status, using minimal temporal context. We also release a public dataset of shadow-related image sequences, to help other researchers further develop shadow detection methods and to enable benchmarking of techniques.
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