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We present new algorithms to perform fast probabilistic collision queries between convex as well as non-convex objects. Our approach is applicable to general shapes, where one or more objects are represented using Gaussian probability distributions. We present a fast new algorithm for a pair of convex objects, and extend the approach to non-convex models using hierarchical representations. We highlight...
This paper presents an extension of Gaussian process implicit surfaces (GPIS) by the introduction of geometric object priors. The proposed method enhances the probabilistic reconstruction of objects from three-dimensional (3-D) pointcloud data, providing a rigorous way of incorporating prior knowledge about objects expected in a scene. The key ideas, including the systematic use of surface normal...
We investigate the perception and reasoning task of answering queries about realistic scenes with objects of daily use perceived by a robot. A key problem implied by the task is the variety of perceivable properties of objects, such as their shape, texture, color, size, text pieces and logos, that go beyond the capabilities of individual state-of-the-art perception methods. A promising alternative...
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