Workpiece Posture Measurement and Intelligent Robot Grasping Based on Monocular Vision

Guo, Qingda; Quan, Yanming; Zhu, Zhongkui

doi:10.1109/icmtma.2016.220

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…As an alternative, many researchers are focusing on visual grabbing detection of untrained items. By only using 2D images, grasping parameters in 2D can be computed by smoothing and edge detection algorithms [6][7]. The position and orientation of objects can be detected by contour information from various perspectives with height transformations [8][9].…”

Section: Introductionmentioning

confidence: 99%

Object detection and pose estimation method by RGB-D camera for robotic grasping

Tang,

Yang,

et al. 2024

Fourth International Conference on Mechanical Engineering, Intelligent Manufacturing, and Automation Technology (MEMAT 2023)

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In the application of vision-guided robotic grasping, 2D position/orientation and 3D depth information are needed. This paper proposes a method for detecting the 4-DoF grasping of unknown objects, which using the combined RGB image and 3D point clouds data by RGB-D camera. The 3-DoF grasp information is obtained by segmenting the target region from the RGB image. Then the depth is converted into a point cloud image by 3D reconstruction. By using an improved RANSAC to fit point cloud planes of the background and object, the height difference between object surface and the planar bottom is determined. The information of Z-axis grasping depth for achieving 4-DoF grasping is determined by RANSAC plane fitting. Experiments are conducted to verify and validate the effectiveness of the proposed method in automated robotic grasping.

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Section: Introductionmentioning

confidence: 99%

Object detection and pose estimation method by RGB-D camera for robotic grasping

Tang,

Yang,

et al. 2024

Fourth International Conference on Mechanical Engineering, Intelligent Manufacturing, and Automation Technology (MEMAT 2023)

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“…This method has attracted extensive attention, as it has the advantages of high precision, non-contact features, real-time measurement, dynamic measurement, simple application, etc. [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Attitude reference transfer method for moving-base based on correlated frames

Nong¹,

Wu²,

Wang³

2023

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022)

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When the monocular visual measurement method is adopted, the initial alignment precision of the moving base tends to be easily interrupted by the surrounding environment. In order to solve this problem, in this study, the authors suggest an initial attitude reference transfer method for moving base based on correlated frames of images. This method utilizes the advantage of inertial reference, namely, high precision of relative attitude measurement within a short time period, transforms the multi-frame sequence images of the planar target to the coordinate system of the initial moment, and averagely reduces noise interruption through more than one image, so that the attitude-calculating precision could be promoted. Simulation research shows that: The attitude reference method based on correlated frames, which is put forward in this study, could effectively reduce random noise interruptions. Within the rotation scope of -1° to 1°, the precision of attitude alignment angles in three directions is better than that of 45''.

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