Abstract
This paper describes an improvement of object pose estimation method for bin-picking problem. A bin-picking system by using industrial robots contributes to factory automization. Industrial robots need to recognize the poses of the object in a bin because they have different poses. We propose a pose estimation method based on point pair feature (PPF) matching. PPF is pose-invariant and has high robustness of occlusion situation. PPF matching performs well in easy bin scenes. However, the successful rate of the pose estimation by PPF matching often decreases in some difficult scenes such as a bin with shiny or black objects because these objects cause bad measurement data with sparse point clouds and low signal-to-noise ratio. Our proposal evaluates PPF reliability by using PPF correspondence between scene data and object database. A PPF which votes for the same pose several times causes false pose generation. Thus, we judge such PPFs unreliable. By rejection of poses generated by using unreliable PPFs, successful rate of pose estimation increased 45% in a difficult bin scene with shiny and black objects. Experimental results show effectiveness of our proposal.
