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