Real-time 3D work-piece tracking with monocular camera based on static and dynamic model libraries

Zhu, Wei; Wang, Peng; Li, Rui; Nie, Xiangli

doi:10.1108/aa-02-2017-018

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…At each location, a similarity score is computed, and the best match is obtained by comparing these similarity scores (Hinterstoisser et al, 2012;Zhe Cao et al, 2016). For example, Zhu et al (2017) present a CAD model-based monocular camera method for real-time 3D tracking of workpieces, which enables the pose estimation by matching a global model library generated offline with images.…”

Section: Related Workmentioning

confidence: 99%

Simulation and deep learning on point clouds for robot grasping

Wang

et al. 2021

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Purpose In this work, the authors aim to provide a set of convenient methods for generating training data, and then develop a deep learning method based on point clouds to estimate the pose of target for robot grasping. Design/methodology/approach This work presents a deep learning method PointSimGrasp on point clouds for robot grasping. In PointSimGrasp, a point cloud emulator is introduced to generate training data and a pose estimation algorithm, which, based on deep learning, is designed. After trained with the emulation data set, the pose estimation algorithm could estimate the pose of target. Findings In experiment part, an experimental platform is built, which contains a six-axis industrial robot, a binocular structured-light sensor and a base platform with adjustable inclination. A data set that contains three subsets is set up on the experimental platform. After trained with the emulation data set, the PointSimGrasp is tested on the experimental data set, and an average translation error of about 2–3 mm and an average rotation error of about 2–5 degrees are obtained. Originality/value The contributions are as follows: first, a deep learning method on point clouds is proposed to estimate 6D pose of target; second, a convenient training method for pose estimation algorithm is presented and a point cloud emulator is introduced to generate training data; finally, an experimental platform is built, and the PointSimGrasp is tested on the platform.

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Section: Related Workmentioning

confidence: 99%

Simulation and deep learning on point clouds for robot grasping

Wang

et al. 2021

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“…e improved PPF matching algorithm realizes the disordered grasping of the object, which is a rough metal casting, by the industrial robot through the measurement of the position and orientation of the target object. A method of target recognition using CAD technology was proposed by Ulrich et al [22] and optimized by Zhu et al [23]. In this paper, Ulrich's method, the original PPF matching algorithm, and the proposed point cloud boundary PPF matching method are compared.…”

Section: Comparative Analysis Of Point Cloud Matching Based On Ppfmentioning

confidence: 99%

6DOF Object Positioning and Grasping Approach for Industrial Robots Based on Boundary Point Cloud Features

Wan

Wang

Xing

et al. 2020

Mathematical Problems in Engineering

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For the three-dimensional (3D) pose estimation of metal blank casts estimate in industrial production process, a novel 6-degree-of-freedom (6DOF) positioning and grasping approach for industrial robots based on boundary features and combining structured light 3D vision and the point cloud matching method is proposed. The proposed approach first uses the Gray code plus grating phase-shift algorithm to reconstruct the 3D surface information of the object. Subsequently, an improved point pair feature (PPF) matching location method based on point cloud boundary extraction is proposed to realize the accurate 6DOF pose estimation of the object. In this method, the point cloud boundary feature is used to optimize the PPF algorithm. Finally, by combining with the point cloud preprocessing process and the improved PPF matching location method, the 6DOF pose positioning of metal blank casting by industrial robots is realized. The proposed approach can accurately complete pose measurement and positioning of objects placed randomly in an open environment. The experimental results demonstrate that the proposed PPF matching location method significantly improves both matching speed and accuracy compared to the traditional PPF algorithm.

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A fast object registration method for augmented reality assembly with simultaneous determination of multiple 2D-3D correspondences

Wang

Liu

et al. 2020

Robotics and Computer-Integrated Manufacturing

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Real-time 3D work-piece tracking with monocular camera based on static and dynamic model libraries

Abstract: 2017) "Real-time 3D work-piece tracking with monocular camera based on static and dynamic model libraries"

Cited by 3 publications

References 28 publications

Simulation and deep learning on point clouds for robot grasping

Simulation and deep learning on point clouds for robot grasping

6DOF Object Positioning and Grasping Approach for Industrial Robots Based on Boundary Point Cloud Features

A fast object registration method for augmented reality assembly with simultaneous determination of multiple 2D-3D correspondences

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