3D object pose estimation for grasping and manipulation is a crucial task in robotic and industrial applications. Robustness and efficiency for robotic manipulation are desirable properties that are still very challenging in complex and cluttered scenes, because 3D objects have different appearances, illumination and occlusion when seen from different viewpoints. This article proposes a Semantic Point Pair Feature (PPF) method for 3D object pose estimation, which combines the semantic image segmentation using deep learning with the voting-based 3D object pose estimation. The Part Mask RCNN ispresented to obtain the semantic object-part segmentation related to the point cloud of object, which is combined with the PPF method for 3D object pose estimation. In order to reduce the cost of collecting datasets in cluttered scenes, a physically-simulated environment is constructed to generate labeled synthetic semantic datasets. Finally, two robotic bin-picking experiments are demonstrated and the Part Mask RCNN for scene segmentation is evaluated through the constructed 3D object datasets. The experimental results show that the proposed Semantic PPF methodimproves the robustness and efficiency of 3D object pose estimation in cluttered scenes with partial occlusions.

用于抓握和操纵的3D对象姿态估计是机器人和工业应用中的关键任务。机器人操纵的鲁棒性和效率是理想的属性，在复杂和混乱的场景中仍然非常具有挑战性，因为从不同的角度看，3D对象具有不同的外观，照明和遮挡。本文提出了一种用于3D对象姿态估计的语义点对特征（PPF）方法，该方法将使用深度学习的语义图像分割与基于投票的3D对象姿态估计相结合。提出了部分遮罩RCNN以获得与对象点云有关的语义对象部分分割，并将其与PPF方法结合用于3D对象姿态估计。为了减少在混乱场景中收集数据集的成本，构建物理模拟环境以生成标记的合成语义数据集。最后，演示了两个机器人垃圾箱拾取实验，并通过构建的3D对象数据集评估了用于场景分割的零件蒙版RCNN。实验结果表明，所提出的语义PPF方法提高了在具有部分遮挡的混乱场景中3D对象姿态估计的鲁棒性和效率。