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Detecting Grasping Opportunities in Range Data

M. Rutishauser and F. Ade
International Society for Photogrammetry and Remote Sensing (ISPRS) Intercommission Workshop: From Pixels to Sequences
March 1995


We have investigated the problem of removing objects from a heap without having recourse to object models. As we are relying on geometric information alone, the use of range data is a natural choice. The objects are to be grasped by a two-fingered gripper and therefore the system has to see opposite patches of the object surface. To ensure this, we use two range views from opposite sides. Each of the two acquired data sets is tessellated into triangles. A merge of them is performed in such a way that a ``mutual approximation'' is achieved in regions with overlap. A single triangular tessellation of the whole data set serves as a primary world representation. This representation is then segmented, i.e., partitioned into assemblies of contiguous triangles which correspond to objects or object parts in the scene. This is done by deleting all jump discontinuity points and all points with a concave curvature above a certain threshold. A connected component labeling completes the final world representation. Two heuristics help the system select a ``focus of action'' which consists of a suitable component. Good grasping point pairs on it are then identified which fulfill certain quality criteria.

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  author = {M. Rutishauser and F. Ade},
  title = {Detecting Grasping Opportunities in Range Data},
  booktitle = {International Society for Photogrammetry and Remote Sensing (ISPRS) Intercommission Workshop: From Pixels to Sequences},
  year = {1995},
  month = {March},
  pages = {237-244},
  volume = {30 5W1},
  keywords = {robot vision, range data, grasping}