The University of British Columbia

The problem of robot visual navigation is bound up with the problem of selecting and extracting salient features or landmarks which can guide the robot safely through its environment. In addition for vision based manipulation tasks for mobile robots we must register the position and orientation of objects to be manipulated. In general identifying the ground plane and distinguishing non-planar obstacles is important for indoor navigation. The large scale industrial robots we study are used in such tasks as forest management or hazardous waste clean up. For these machines identification and registration of vertical cylinders representing trees to be avoided or 45 gal. drums to be manipulated is critical. Instead of laborious reconstruction of surfaces to find these critical plane and cylinder shape primitives we propose that the subspace defined by the disparity gradients of visible surfaces can be used to extract them efficiently. We demonstrate the use of these features, which are easily extracted in real time using a high speed pipeline processor