A multiple kernel convolution score method for bin picking of plastic packed object

In this paper, an object detection and localization method for bin picking of plastic wrapped objects is described. Since such objects are deformable and have non-Lambertian surfaces, it is difficult to apply conventional feature point approaches or edge based template matching. To solve this problem, we propose a new method which is called “KCS (kernel convolution score)”. It measures the total score of convolution between local image and multiple kernels which are generated according to the characteristics of the target object. The local image having maximum score is considered as the most promising object for picking. In the last part of this paper, the limitation of existing edge and template matching in the given problem is discussed with the experimental results. Performance evaluation shows that the proposed method could localize the object which is appropriate for picking up with 91.67% success rate.