A RealTime 3D Measurement System for the Blast Furnace Burden Surface Using HighTemperature Industrial Endoscope Capturing the threedimensional (3D) shape of the burden surface of a blast furnace (BF) in realtime with high accuracy is crucial for improving gas flow distribution, optimizing coke operation, and stabilizing BF operation. However, it is difficult to perform 3D shape measurement of the burden surface in realtime during the ironmaking process because of the hightemperature, highdust, and lightless enclosed environment inside the BF. To solve this problem, a realtime 3D measurement system is developed in this study by combining an industrial endoscope with a virtual multihead camera array 3D reconstruction method. First, images of the original burden surface are captured using a purposebuilt industrial endoscope. Second, a novel micropixel luminance polarization method is proposed and applied to compensate for the heavy noise in the backlit images due to high dust levels and poor light in the enclosed environment. Third, to extract depth information, a multifeaturebased depth key frame classifier is designed to filter out images with high levels of clarity and displacement. Finally, a 3D shape burden surface reconstruction method based on a virtual multihead camera array is proposed for capturing the realtime 3D shape of the burden surface in an operational BF. The results of an industrial experiment illustrate that the proposed method can measure the 3D shape of the entire burden surface and provide reliable burden surface shape information for BF control.

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.