论文标题
3D对象细分人类挑选货架箱，深度学习和占用素网格
3D Object Segmentation for Shelf Bin Picking by Humanoid with Deep Learning and Occupancy Voxel Grid Map
论文作者
论文摘要
在狭窄空间（例如架子箱）中挑选对象是人类从环境中提取目标对象的重要任务。但是，在这些情况下，相机和对象之间存在许多遮挡，这使得由于缺乏三维传感器输入，因此很难将目标对象分为三个维度。我们通过多个相机角度积累分割结果解决了这个问题，并生成目标对象的体素模型。我们的方法由两个组成部分组成：首先是使用卷积网络的输入图像的对象概率预测，其次是生成用于对象分割的Voxel Grid映射。我们通过采摘任务实验评估了狭窄架子箱中目标对象的方法。我们的方法即使在遮挡中也会生成密集的3D对象段，而真正的机器人成功从狭窄的空间挑选了目标对象。
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.
