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Reliable manipulation of everyday household objects is essential to the success of service robots. In order to accurately manipulate these objects, robots need to know objects' full 6-DOF pose, which is challenging due to sensor noise, clutters and occlusions. In this paper, we present a new approach for effectively guessing the object pose given an observation of just a small patch of the object,...
This paper presents a means to approximate an object's pose, suitable for initialization of the Iterative Closest Point algorithm. The class of problems considered is objects lying stably on a planar surface, for which a relatively small number of pose types are possible. Within each pose type, the registration problem is reduced to 3 dimensions. Using contours computed from horizontal slices, it...
A means for fast and accurate indoor localization using point-cloud data is presented. A sparse environment model is proposed, comprised of a list of planar patches. It is shown that, with this simplified model, incoming point-cloud data can be associated rapidly with one (or none) of the constituent planes, and the environment model can be fit to the clustered sample points algebraically in a least-squares...
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