使用几何和光度局部特征的三维点云鲁棒描述符

Hyoseok Hwang, S. Hyung, Sukjune Yoon, K. Roh
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引用次数: 11

摘要

为了熟练地处理各种物体,机器人强烈需要具有鲁棒感知能力。在本文中,我们提出了一种使用三维特征描述符来识别物体并估计其六自由度姿态的新方法,称为几何和光度局部特征(GPLF)。该描述符利用RGB-D相机的三维点云的几何和光度信息,并将这些信息整合到有效的描述符中。在混乱的场景中,无论物体的形状或外观等特征如何,GPLF都表现出鲁棒的判别性能。实验结果表明,该方法对目标的分类和识别效果良好。姿态估计的鲁棒性和稳定性足以满足机器人对目标的操纵。我们还将所提出的方法与之前使用具有代表性的大规模RGB-D对象数据集的对象部分信息的方法进行了比较。
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Robust descriptors for 3D point clouds using Geometric and Photometric Local Feature
The robust perception of robots is strongly needed to handle various objects skillfully. In this paper, we propose a novel approach to recognize objects and estimate their 6-DOF pose using 3D feature descriptors, called Geometric and Photometric Local Feature (GPLF). The proposed descriptors use both the geometric and photometric information of 3D point clouds from RGB-D camera and integrate those information into efficient descriptors. GPLF shows robust discriminative performance regardless of characteristics such as shapes or appearances of objects in cluttered scenes. The experimental results show how well the proposed approach classifies and identify objects. The performance of pose estimation is robust and stable enough for the robot to manipulate objects. We also compare the proposed approach with previous approaches that use partial information of objects with a representative large-scale RGB-D object dataset.
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