Fast and precise 6D pose estimation of textureless objects using the point cloud and gray image

Pan Wang; Zhu Feng; Hao Yingming; Zhang Limin

摘要

Pose estimation for textureless objects is a challenging task in robotics, due to the scanty information of surfaces. In this paper, we design a vision system for fast and precise position and orientation measurement of textureless objects with a depth camera and a CCD camera. The corresponding process includes two parts: object segmentation in the point cloud and pose measurement in the gray image. Considering the relation between the object and its fixed panel, we first extract the panel in the point cloud by combining a random sample consensus algorithm with local surface normal. We then coarsely segment the possible area of the object based on an oriented bounding box. Finally, we transform the point cloud coordinates into the image coordinate system, and measure the precise pose of the object with a view-based matching method. Two types of cameras are brought together to make their respective advantages play well. The downscale method and coarse-to-fine strategy are utilized sufficiently to increase efficiency. Experiments show that our vision system achieves high pose measurement precision and enough efficiency. The average error is less than 2 mm for x and y, less than 4 mm for z, and 1 degrees in orientation, meeting the requirements for our robotic grasping task. (C) 2018 Optical Society of America

机译：由于表面的神出信息，Textulless对象的姿态估计是机器人中的一个具有挑战性的任务。在本文中，我们设计了一种具有深度相机和CCD摄像机的快速和精确位置和定向测量的视觉系统，以及带有深度相机和CCD相机的定位测量。相应的过程包括两个部分：点云中的对象分割和灰色图像中的姿势测量。考虑到对象和固定面板之间的关系，我们首先通过将随机样本共识算法与局部正常的随机样本共识算法组合来提取点云中的面板。然后，我们基于面向定向的边界框粗略地分段对象的可能区域。最后，我们将点云坐标转换为图像坐标系，并通过基于视图的匹配方法测量对象的精确姿势。两种类型的相机汇集在一起，以使其各自的优势良好。较低的方法和粗致细策略充分利用以提高效率。实验表明，我们的视觉系统实现了高姿态测量精度和足够的效率。 X和Y的平均误差小于2 mm，Z小于4 mm，方向1度，满足我们机器人掌握任务的要求。（c）2018年光学学会

Fast and precise 6D pose estimation of textureless objects using the point cloud and gray image

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