RANGE IMAGE SEGMENTATION WITH APPLICATIONS TO ROBOT BIN-PICKING USING VACUUM GRIPPER

Widespread interest in factory automation and robotics has spurred research in the field of machine vision. Most previous research work has used intensity images. In the last few years range data analysis has become very popular. Range data acquisition is slower than that of intensity images. However, depth information allows us to handle a wide variety of applications more efficiently. A new segmentation algorithm that can be used for robot applications is presented. The input images are dense range data of industrial parts. The image is segmented into a number of surfaces. The segmentation algorithm uses the residual analysis to detect edges. Then a region growing technique is used to obtain the final segmented image. Most of the research in 3-D vision has centered on recognizing objects. The use of the segmentation output for determining the best holdsite position and orientation is studied. As compared to techniques based on intensity images the use of range images simplifies the holdsite determination. This information can then be used to instruct the robot to grip the object and move it to the required position. The performance of the algorithm on a number of range images is presented. © 1990 IEEE