Binpicking of Randomly Piled Shiny Industrial Objects Using Light Transport Matrix Estimation * The use of robots in automated factories requires accurate binpicking to ensure that objects are correctly identified and selected. In the case of objects with multiple reflections from their surfaces, this is a challenging task. We attempted to address this problem by developing a 3D measurement method based on a Light Transport Matrix (LTM), which can be applied to shiny objects or semitransparent objects. The study presented herein evaluates the accuracy of the proposed method as well as the method for 3D pose estimation we previously reported, by examining a binpicking task, which is a wellknown robot application in factory automation. There is considerable demand for automated binpicking systems for general objects. However, in the use of costeffective measurement systems, some objects such as shiny metallic objects continue to prove problematic in terms of binpicking because of the difficulty to measure their shapes accurately. Our 3D measurement method uses only a projectorcamera system; thus, it is costeffective, and it does not require any special optical system. It is based on fast LTM sparse estimation. We previously demonstrated that this approach can measure the 3D shape of metallic objects and showed that our pose estimation method is applicable to binpicking. However, we did not verify its accuracy with the application of 3D robot vision. In this study, we integrate these two methods, and demonstrate that our 3D measurement method, in combination with our pose estimation work, can successfully accomplish binpicking tasks involving shiny metallic industrial objects. Ultimately, we achieved 100 % picking success for 5 scenes including 15 pieces. We concluded that our proposed methods are sufficiently accurate to carry out binpicking tasks in automated factory environments.

Largescale 6D Object Pose Estimation Dataset for Industrial BinPicking In this paper, we introduce a new public dataset for 6D object pose estimation and instance segmentation for industrial binpicking. The dataset comprises both synthetic and realworld scenes. For both, point clouds, depth images, and annotations comprising the 6D pose (position and orientation), a visibility score, and a segmentation mask for each object are provided. Along with the raw data, a method for precisely annotating realworld scenes is proposed.To the best of our knowledge, this is the first public dataset for 6D object pose estimation and instance segmentation for binpicking containing sufficiently annotated data for learningbased approaches. Furthermore, it is one of the largest public datasets for object pose estimation in general. The dataset is publicly available at http:www.binpicking.aien dataset.html.