Abstract
This paper aims at presenting the STRONGMAR Summer School 2016 that took place at the Base Naval de Lisboa, of the Portuguese Navy. The STRONGMAR project ideal motivates the development of maritime and marine science research and technology through the knowledge transfer between INESC TEC and promising, and prestigious, leading research European institutions. This process takes place through theoretical lectures and training, and via experimental application of the concepts discussed in order to further develop technology related to the sea environment. The practical application of the STRONGMAR project ideal takes place during events such as summer schools, winter schools, thematic workshops and scientific conferences. The STRONGMAR Summer School 2016 approaches the subject of "Introduction to Advanced Marine Technologies", providing a strong component of practical applications in underwater archaeology. It develops the study of unmanned systems applied to underwater archaeology, through the use of unmanned underwater vehicles. As a whole, this paper describes the Summer School experience, providing some results and greater insight on the topic of underwater archaeology.

Abstract
The autonomy of robotic underwater vehicles is dependent on the ability to perform long-term and long-range missions without need of human intervention. While current state-of-the-art underwater navigation techniques are able to provide sufficient levels of precision in positioning, they require the use of support vessels or acoustic beacons. This can pose limitations on the size of the survey area, but also on the whole cost of the operations. Terrain Based Navigation is a sensor-based navigation technique that bounds the error growth of dead reckoning using a map with terrain information, provided that there is enough terrain variability. An obvious advantage of Terrain Based Navigation is the fact that no external aiding signals or devices are required. Because of this unique feature, terrain navigation has the potential to dramatically improve the autonomy of Autonomous Underwater Vehicles (AUVs). This paper consists on a comprehensive survey on the recent developments for Terrain Based Navigation methods proposed for AUVs. The survey includes a brief introduction to the original Terrain Based Navigation formulations, as well as a description of the algorithms, and a list of the different implementation alternatives found in the literature. Additionally, and due to the relevance, Bathymetric SLAM techniques will also be discussed.

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Recent advances in visual motion detection and interpretation have made possible the rising of new robotic systems for autonomous and active surveillance. In this line of research, the current work discusses motion perception by proposing a novel technique that analyzes dense flow fields and distinguishes several regions with distinct motion models. The method is called Wise Optical Flow Clustering (WOFC) and extracts the moving objects by performing two consecutive operations: evaluating and resetting. Motion properties of the flow field are retrieved and described in the evaluation phase, which provides high level information about the spatial segmentation of the flow field. During the resetting operation, these properties are combined and used to feed a guided segmentation approach. The WOFC requires information about the number of motion models and, therefore, this paper introduces a model selection method based on a Bayesian approach that balances the model's fitness and complexity. It combines the correlation of a histogram-based analysis with the decay ratio of the normalized entropy criterion. This approach interprets the flow field and gives an estimative about the number of moving objects. The experiments conducted in a realistic environment have proved that the WOFC presents several advantages that meet the requirements of common robotic and surveillance applications: is computationally efficient and provides a pixel-wise segmentation, comparatively to other state-of-the-art methods.

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