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Picking objects in a narrow space such as shelf bins is an important task forhumanoid to extract target object from environment. In those situations,however, there are many occlusions between the camera and objects, and thismakes it difficult to segment the target object three dimensionally because ofthe lack of three dimentional sensor inputs. We address this problem withaccumulating segmentation result with multiple camera angles, and generatingvoxel model of the target object. Our approach consists of two components:first is object probability prediction for input image with convolutionalnetworks, and second is generating voxel grid map which is designed for objectsegmentation. We evaluated the method with the picking task experiment fortarget objects in narrow shelf bins. Our method generates dense 3D objectsegments even with occlusions, and the real robot successfuly picked targetobjects from the narrow space.

3D Object Segmentation for Shelf Bin Picking by Humanoid with Deep Learning and Occupancy Voxel Grid Map