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.

中文翻译：

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基于深度学习和占用体素网格图的 Humanoid 货架箱拣选的 3D 对象分割

在狭窄空间（例如货架箱）中拾取物体是类人机器人从环境中提取目标物体的一项重要任务。然而，在这些情况下，相机和物体之间存在许多遮挡，由于缺乏三维传感器输入，这使得难以对目标物体进行三维分割。我们通过累积多个摄像机角度的分割结果并生成目标对象的体素模型来解决这个问题。我们的方法由两个部分组成：首先是使用卷积网络对输入图像进行对象概率预测，其次是生成专为对象分割而设计的体素网格图。我们通过对窄货架箱中的目标物体进行拣选任务实验来评估该方法。