Bin picking for the objects of non-Lambertian reflectance without using an explicit object model

Objects with non-Lambertian reflecting surfaces suffer severe appearance changes according to the change of observer's angle of view. Thus it is difficult to obtain a consistent object model. This paper addresses the problem of bin picking for such objects without using explicit object models. Due to the above-mentioned reasons, traditional pattern matching approaches are difficult to apply. Furthermore, 3D sensors often fail to capture these objects correctly, which also disables the approaches based on 3D object models. Considering these limitations, this paper proposes a novel algorithm that tries to find a reasonable pickup point by investigating the status of the pile in the bin instead of trying to perfectly isolate single object from the pile and measure full pose information. For the purpose, feature point matching between stereo cameras is adopted to retrieve approximate 3D information of the pile. Additionally, by associating these corresponding point pairs with the binary patches around the point, corresponding binary patch pairs are detected, which is evaluated by a proposed objective function to select the most appropriate candidate for the pickup operation.