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Pose-Invariant Object Recognition
Robust scene interpretation by means of machine vision is a key factor in various new applications in robotics. Part of this problem is the efficient recognition and classification of previously known three-dimensional (3D) shapes in arbitrary scenes. So far, heavily constrained conditions have been utilized, or otherwise solutions have not been achieved in real time.
Photorealistic 3D modelling
Today in robotics, computer vision in a broad sense can be regarded as the key technology for realizing systems with an enhanced level of autonomy. In this domain, we tackle problems from a wide methodological range, from image-based tracking to scene understanding and world modeling. With such a broad scope, we can address application demands as diverse as rapid visual servoing and flexible adaptive behavior for a robot system, or generation of photo-realistic representations in a virtual-reality context.
Tracking and Servoing
When a rigid object moves in 3D space relative to a camera, it is often interesting to know how its relative pose changes in its full 6 degrees of freedom (DoF). The problem of 6-DoF tracking arises in the context of numerous applications within and beyond robotics.
One key aspect necessary for a successful minimally invasive intervention is preoperative planning, done by the surgeon in order to prepare the intervention and to decide about the best access to the surgical site. In case of robotically assisted interventions the results of these decisions must be transferred also to the robotic equipment.
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