Object recognition using eigenvectors

A method for object recognition and pose estimation for robotic bin picking is presented. The approach discussed is a variant on current approaches to eigenimage analysis. Compared to traditional approaches which use object geometry only (shape invariants), the implementation described uses the eigenspace determined by processing the eigenvalues and eigenvectors of the image set. The image set is obtained by varying pose whilst maintaining a constant level of illumination in space, and the eigenspace is computed for each object of interest. For an unknown input image, the recognition algorithm projects this image to each eigenspace and the object is recognised using space partitioning methods which determine the object and the position in space. Several experimental results have been obtained to demonstrate the robustness of this method when applied to the robotic bin picking task.