Object Recognition and Pose Estimation from RGB-D Data Using Active Sensing
Object Recognition and Pose Estimation from RGB-D Data Using Active Sensing
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DOI：
10.1109/aim52237.2022.9863241
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
U. Manawadu;Shishiki Keito;K. Naruse
中科院分区：
文献类型：
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作者：
U. Manawadu;Shishiki Keito;K. Naruse
工厂自动化在生产现场迅速发展。另一方面，有人提到，在潜伏着许多危险的工厂里，检查工作仍然经常由人工完成。尽管如此，通过用机器人自动化代替许多需要灵活性的任务，例如打开和关闭阀门，可以确保安全性。因此，需要更精确的目标对象的对象识别和姿态估计。本文考虑了利用机器人手臂从不同角度自动开启和关闭地球仪阀门的任务。开发了一个系统，并介绍了一个精确的目标识别和姿态估计使用RGB-D相机连接到机器人手臂。该系统包括三个主要的子系统，目标识别，姿态估计，手臂控制。利用RGB-D摄像机对地球仪瓣膜进行基于颜色的分割，通过检测特征，利用方向直方图的颜色特征（CSHOT）算法检测瓣膜区域。然后应用奇异值分解对阀的精确位置进行估计。通过考虑机械臂与地球仪阀的夹角，将0° ~ 30°作为前模态，将30° ~ 90°作为侧模态。从侧面检测物体是这一研究领域中的一个新课题。随机抽样一致性（RANSAC）算法用于验证瓣膜前模式的位姿估计。采用质心连接和位姿积分Hough-Voting方法对侧模位姿估计进行验证。从实验结果可以看出，该系统从侧面模式到正面模式都能准确地检测到地球仪阀门的目标识别和位姿估计。
Factory automation has been growing rapidly in production sites. On the other hand, it is mentioned that inspection work is still often done manually in factories where many dangers lurk. Although, safety can be ensured by substituting robot automation for many tasks that require dexterity such as opening and closing valves. Therefore, more accurate object recognition and pose estimation of the target object is required. In this paper, the task of automatically opening and closing a globe valve using a robot arm from different angles is considered. A system was developed and introduced to make a precise object recognition and pose estimation using an RGB-D camera attached to the robot arm. The system consists of three main sub-systems for object recognition, pose estimation, and arm-control. By using an RGB-D camera, it was possible to make color-based segmentation of the globe valve to the detect valve area using the Color Signature Of Histograms of Orientations (CSHOT) algorithm by detecting features. Then apply singular value decomposition to the precise position of the valve. By considering the angle of the robot arm to the globe valve, from 0° to 30° was taken as the front mode, and from 30° to 90° was taken as the side mode. Detecting the objects from the side is novel in this research area. RANdom SAmple Consensus (RANSAC) algorithm was used to verify the pose estimation of the front mode of the valve. Centroid connection and Pose Integration Hough-Voting methods were used to verify the pose estimation of the side mode. From the results, it can be concluded that the system is accurately detecting object recognition and pose estimation of a globe valve from the side mode to the front mode.