Detection and localization of pallets on shelves using a wideangle camera This paper proposes a method to detect and localize pallets on shelves using a wideangle camera mounted on a vehicle several meters away along the shelves. Although wideangle observation is a significant advantage in navigation, objects appear small and largely distorted in the observed image, thus creating a severe disadvantage for the detection and localization of objects. To solve this problem, we propose reprojecting wideangle views onto a threedimensional (3D) plane to obtain a good projection image for object detection, in which pallets appear in the same shape and size regardless of the location in the image. Since the image projected on the plane coplanar to the palletu0027s front surface satisfy the above condition, we approximate the projection plane using the front surface of the shelves with pallets on it. Owing to the constant appearance of the pallet on the projected image, it can be robustly detected using the matching with a fixed template. When a pallet is detected, it is threedimensionally localized from the equation of the 3D plane of the projection plane and its 2D location in the projected image. Experiments using empirically captured scenes demonstrated the detection and localization ability of the proposed method. Despite small area in the original wideangle view, pallets of interest were discriminatively detected from others and localized with acceptable accuracy.

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.