Multi-View-Based Pose Estimation and Its Applications on Intelligent Manufacturing

Yang, Haiwei; Jiang, Pingyu; Wang, Fei

doi:10.3390/s20185072

Cited by 4 publications

(3 citation statements)

References 31 publications

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“…Common 2D image segmentation algorithms include the binarisation method, watershed method [ 19 ], and edge method [ 20 ]. Figure 6 shows the results of the test image segmentation process using the edge method.…”

Section: Target Workpiece Segmentation and 3d Matching Methodsmentioning

confidence: 99%

Research on the Industrial Robot Grasping Method Based on Multisensor Data Fusion and Binocular Vision

Xie

2022

Computational Intelligence and Neuroscience

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At present, most of the handling industrial robots working on the production line are operated by teaching or preprogramming, which makes the flexibility of the production line poor and does not meet the flexible production requirements of the material handling system. This study proposes a solution based on adding computer binocular vision to a five-axis industrial robot system. A simple and high-precision binocular camera calibration method is proposed, the kinematics of the five-axis robot is analyzed, and the target positioning is realized; the communication between the upper and lower robots is realized through Ethernet. According to the specific target, the grasping scheme of the gripper was designed; the control software was developed using two schemes. Visual control is carried out by operating specific buttons on the control panel, and visual control is carried out by executing the macrovariable program, finally realizing the joint fusion of multisensor data and binocular vision.

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Section: Target Workpiece Segmentation and 3d Matching Methodsmentioning

confidence: 99%

Research on the Industrial Robot Grasping Method Based on Multisensor Data Fusion and Binocular Vision

Xie

2022

Computational Intelligence and Neuroscience

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“…In addition, among robotic assembly studies, there are many assembly scenarios, such as peg-in-hole assembly (Pauli et al, 2001 ; Yang et al, 2020 ), chute assembly (Peternel et al, 2018 ), bolt assembly (Laursen et al, 2015 ), etc. The peg-in-hole assembly is the most common of all assembly tasks and the one most commonly studied.…”

Section: Related Workmentioning

confidence: 99%

Single-Camera Multi-View 6DoF pose estimation for robotic grasping

2023

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Accurately estimating the 6DoF pose of objects during robot grasping is a common problem in robotics. However, the accuracy of the estimated pose can be compromised during or after grasping the object when the gripper collides with other parts or occludes the view. Many approaches to improving pose estimation involve using multi-view methods that capture RGB images from multiple cameras and fuse the data. While effective, these methods can be complex and costly to implement. In this paper, we present a Single-Camera Multi-View (SCMV) method that utilizes just one fixed monocular camera and the initiative motion of robotic manipulator to capture multi-view RGB image sequences. Our method achieves more accurate 6DoF pose estimation results. We further create a new T-LESS-GRASP-MV dataset specifically for validating the robustness of our approach. Experiments show that the proposed approach outperforms many other public algorithms by a large margin. Quantitative experiments on a real robot manipulator demonstrate the high pose estimation accuracy of our method. Finally, the robustness of the proposed approach is demonstrated by successfully completing an assembly task on a real robot platform, achieving an assembly success rate of 80%.

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“…For instance, [29] proposed a visual guidance system for robotic a peg-in-hole application consisting of four cameras: two in an eye-to-hand configuration for the localization of the robotic tool, while the others are in an eye-in-hand configuration and are used for alignment of the tool with reference holes. A multi-view approach was presented in [30] for the localization of target objects in a pick-and-place framework with sub-millimeter level accuracy. The versatility of vision systems have also enabled other uses in navigation, guidance, and calibration systems of mobile industrial robots [31,32,33].…”

Section: Introductionmentioning

confidence: 99%

Neuromorphic Vision Based Control for the Precise Positioning of Robotic Drilling Systems

Ayyad¹,

Halwani²,

Swart³

et al. 2022

Preprint

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The manufacturing industry is currently witnessing a paradigm shift with the unprecedented adoption of industrial robots, and machine vision is a key perception technology that enables these robots to perform precise operations in unstructured environments. However, the sensitivity of conventional vision sensors to lighting conditions and high-speed motion sets a limitation on the reliability and work-rate of production lines. Neuromorphic vision is a recent technology with the potential to address the challenges of conventional vision with its high temporal resolution, low latency, and wide dynamic range. In this paper and for the first time, we propose a novel neuromorphic vision based controller for faster and more reliable machining operations, and present a complete robotic system capable of performing drilling tasks with sub-millimeter accuracy. Our proposed system localizes the target workpiece in 3D using two perception stages that we developed specifically for the asynchronous output of neuromorphic cameras. The first stage performs multi-view reconstruction for an initial estimate of the workpiece's pose, and the second stage refines this estimate for a local region of the workpiece using circular hole detection. The robot then precisely positions the drilling endeffector and drills the target holes on the workpiece using a combined position-based and imagebased visual servoing approach. The proposed solution is validated experimentally for drilling nutplate holes on workpieces placed arbitrarily in an unstructured environment with uncontrolled lighting. Experimental results prove the effectiveness of our solution with an average positional errors of less than 0.1 mm, and demonstrate that the use of neuromorphic vision overcomes the lighting and speed limitations of conventional cameras. The findings of this paper identify neuromorphic vision as a promising technology that can expedite and robustify robotic manufacturing processes in line with the requirements of the fourth industrial revolution.

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Multi-View-Based Pose Estimation and Its Applications on Intelligent Manufacturing

Cited by 4 publications

References 31 publications

Research on the Industrial Robot Grasping Method Based on Multisensor Data Fusion and Binocular Vision

Research on the Industrial Robot Grasping Method Based on Multisensor Data Fusion and Binocular Vision

Single-Camera Multi-View 6DoF pose estimation for robotic grasping

Neuromorphic Vision Based Control for the Precise Positioning of Robotic Drilling Systems

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