@conference {281,
	title = {Autonomous underwater grasping using multi-view laser reconstruction},
	booktitle = {OCEANS 2017 - Aberdeen},
	year = {2017},
	month = {10/2017},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Aberdeen (UK)},
	abstract = {<p>Autonomous manipulation in underwater scenarios is a highly complex task which is still poorly studied, but with a growing interest in the last years. Two of the main phases of this problem are the detection and characterization of the object which is going to be manipulated, and the planning of this manipulation. This paper presents, on the one hand, the results of a multi-view laser reconstruction approach used in a real system. This approach consists in attaching a laser emitter and a camera in the forearm of a robotic arm. Then, moving the arm, the scene is scanned and reconstructed. This configuration allows the user to reconstruct autonomously a scene from different points of view and with a high precision. On the other hand, a grasp planning algorithm has been described. This method makes possible to autonomously detect the object of interest from a scene with multiple objects, and to choose which is the best arm configuration in order to manipulate it.</p>
},
	keywords = {arm configuration, Autonomous Manipulation, autonomous underwater grasping, autonomous underwater vehicles, camera, Cameras, grasp planning algorithm, Grasping, image reconstruction, laser emitter, Lasers, manipulators, multiview laser reconstruction approach, object characterization, object detection, Planning, robot vision, Robot vision systems, robotic arm, scene reconstruction, Three-dimensional displays},
	isbn = {978-1-5090-5278-3 },
	doi = {10.1109/OCEANSE.2017.8084797},
	url = {https://doi.org/10.1109/OCEANSE.2017.8084797},
	author = {Antonio Pe{\~n}alver and J. J. Fern{\'a}ndez and Sanz, Pedro J.}
}
@conference {282,
	title = {Increasing underwater manipulation autonomy using segmentation and visual tracking},
	booktitle = {OCEANS 2017 - Aberdeen},
	year = {2017},
	month = {10/2017},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Aberdeen (UK)},
	abstract = {<p>The present research in underwater robotics aims to increase the autonomy of manipulation operations in fields such as archaeology or biology, that cannot afford costly underwater interventions using traditional Remote Operated Vehicles (ROV). This paper describes a work towards the long term goal of autonomous underwater manipulation. Autonomous grasping, with limited sensors and water conditions which affect the robot systems, is a growing skill in underwater scenarios. Here we present a framework that uses vision, segmentation, user interfaces and grasp planning to perform visually guided manipulation to improve the specification of grasping operations. With it, a user commands and supervises the robot to recover cylinder shaped objects, a very common restriction in archaeological scenarios. This framework, though, can be expanded to detect other kind of objects. Information of the environment is gathered with stereo cameras and laser reconstruction methods to obtain a model of the object\&$\#$39;s graspable area. A RANSAC segmentation algorithm is used to estimate the model parameters and the best grasp is presented to the user in an intuitive user interface. The grasp is then executed by the robot. This approach has been tested in simulation and in water tank conditions.</p>
},
	keywords = {archaeological scenarios, archaeology, autonomous grasping, autonomous underwater manipulation, Cameras, Computational modeling, cylinder shaped objects, grasping operations, image reconstruction, image segmentation, intuitive user interface, manipulation operations, manipulators, mobile robots, RANSAC segmentation algorithm, Remote Operated Vehicles, remotely operated vehicles, robot systems, robot vision, Robots, ROV, stereo image processing, Storage tanks, Three-dimensional displays, underwater interventions, underwater manipulation autonomy, underwater robotics, underwater scenarios, underwater vehicles, user interfaces, visual tracking, Visualization, visually guided manipulation, water conditions, water tank conditions},
	isbn = {978-1-5090-5278-3 },
	doi = {10.1109/OCEANSE.2017.8084762},
	url = {https://doi.org/10.1109/OCEANSE.2017.8084762},
	author = {Fornas, David and Sanz, Pedro J. and M. Vincze}
}
