Presents a reliable and robust approach to the problem of grasping arbitrarily shaped 3-D objects from a pile. The approach adheres to the paradigm of purposive vision, which says that one should only extract as much information as it is needed to perform a certain task, e.g. grasping, while a complete and precise recovery of the shape of the objects is not necessary. The authors show that planar patches obtained by the recover-and-select paradigm contain enough information to enable generating object hypotheses and to estimate grasping points for the objects. The authors present some results for objects with polyhedral as well as with curved surfaces obtained on real range images.

Range data offer a direct way to produce shape descriptions of surfaces. Typically single range images have the form of a graph surface z=g(z,y) and thus suffer from occlusion. One can reduce this problem by taking several images from different locations and merging them together. The result is a real 3-D description of the object's surface. In this paper we address several problems that result from the 2 1/2 -D to 3-D transition. We present an algorithm which is able to merge depth images of an arbitrary shaped object using a highly local approach. An explicit sensor error model is used to support the merging. A key feature of our algorithm is the ability to update its result by an additional new view. Thus, it is possible to gradually improve the surface description in regions of high noise.>