For intuitive human-robot collaboration, the robot must quickly adapt to the human behavior. To this end, we propose a multimodal sensor-based control framework, enabling a robot to recognize human intention, and consequently adapt its control strategy. Our approach is marker-less, relies on a Kinect and on an on-board camera, and is based on a unified task formalism. Moreover, we validate it in a mock-up industrial scenario, where human and robot must collaborate to insert screws in a flank.
The underwater 3D reconstruction cartography has made great progress in the last decade. The work presented in this paper is about the analysis and 3D reconstruction of archeological objects. Using a calibrated single camera and an uncalibrated system, we propose to describe a method to perform the Euclidian 3D reconstruction of unknown objects. A comparison of two methods is presented and tested on synthetic and real underwater pictures. Filters are proposed to simulate underwater environment and inherent problems. Finally, robust and stable features have been extracted from underwater pictures and used to perform the 3D model.
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