A Calibration and 3-D Measurement Method for an Active Vision System With Symmetric Yawing Cameras

Xu, De; Zhang, Di; Liu, Xiaoyang; Ma, Liping

doi:10.1109/tim.2021.3092517

Cited by 11 publications

(7 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some studies have used polynomials and Gaussian processes to fit the relationship between the camera error and measured values. [43,44] Similarly, this study proposed an m-order error model for the camera with reference to the measurement principle and empirical error distribution of the 3D camera, [11,12,42] as follows 8 > > > < > > > :…”

Section: Data Generationmentioning

confidence: 99%

“…Because the workpiece size often exceeds the field of view (FOV) of the 3D camera, the local point clouds of the workpiece are captured from multiple sampled poses, and these point clouds are then transformed into the robot base coordinate system to generate a complete point cloud of the workpiece.However, the measurement accuracy of 3D robot measurement systems is lower than that of CMMs, primarily because of errors related to the robot pose, [8,9] 3D camera installation, [10] and 3D camera measurement. [11,12] If these issues are addressed, a robot measurement system can fully replace CMMs. Markerbased [13,14] and calibration-based [15,16] methods are two mainstream approaches for overcoming this limitation inherent in 3D robot measurement systems.…”

mentioning

confidence: 99%

“…However, the measurement accuracy of 3D robot measurement systems is lower than that of CMMs, primarily because of errors related to the robot pose, [8,9] 3D camera installation, [10] and 3D camera measurement. [11,12] If these issues are addressed, a robot measurement system can fully replace CMMs. Markerbased [13,14] and calibration-based [15,16] methods are two mainstream approaches for overcoming this limitation inherent in 3D robot measurement systems.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Simple and High‐Precision Hand–Eye Calibration for 3D Robot Measurement Systems

Wu,

Zang,

Bai

et al. 2023

Advanced Intelligent Systems

View full text Add to dashboard Cite

Three dimensional (3D) robot measurement systems, with binocular planar structured light cameras (3D cameras) installed at the terminal flange of robots, are widely used to measure complete workpieces by stitching point clouds obtained from various sampled poses. To ensure accurate stitching, hand–eye calibration is necessary. However, 3D cameras often demonstrate low precision in measuring jumping edges and vertices, resulting in challenges when selecting appropriate calibrators and calibration methods for hand–eye calibration. We propose a simple and high‐precision hand–eye calibration method for 3D robot measurement systems. Initially, a single sphere is utilized as the calibrator, and its poses are observed using specific robotic motions. Subsequently, the hand–eye calibration problem is formulated as a well‐known equation, , relying solely on the high‐precision relative pose of the robot. Finally, a novel simultaneous solution for and the corresponding closed‐form initial solution are introduced. Simulations and experiments confirm that the proposed method exhibits high noise resistance and achieves high‐precision calibration, even with a limited calibration data quantity. Compared with traditional methods, the fitting error of the proposed method can be reduced from over 0.9 mm to less than 0.6 mm.

show abstract

Section: Data Generationmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Simple and High‐Precision Hand–Eye Calibration for 3D Robot Measurement Systems

Wu,

Zang,

Bai

et al. 2023

Advanced Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…In view of this, we believe image correction is very necessary. From (13), we know that it is a dual higher order equation. Therefore, solving the ideal undistorted coordinates (u, v) directly is very difficult.…”

Section: Image Distortion Correctionmentioning

confidence: 99%

“…On this foundation, Yang [11] developed a new active vision calibration technique from four or five groups of the orthogonal motions. In recent years, active vision calibration has been developed further [12][13][14], which can easily solve problems and obtain parameters. This type of camera calibration method usually requires very sophisticated equipment and therefore calibration equipment is relatively expensive.…”

Section: Introductionmentioning

confidence: 99%

A New Camera Calibration Technique for Serious Distortion

Huang

Zou

2022

Processes

View full text Add to dashboard Cite

A new camera calibration technique based on serious distortion is proposed, which only requires the camera to observe the plane pattern in an arbitrary azimuth. It uses the geometrical imaging principle and radial distortion model to acquire radial lens distortion coefficient and the image coordinate (u0, v0), and then solves the linear equation aiming at the other parameters of the camera. This method has the following characteristics: Firstly, the position of the camera and the plane is arbitrary, and the technique needs only a single observation for plane pattern. Secondly, it is suitable for camera calibration with serious distortion. Thirdly, it does not need expensive ancillary equipment, accurate movement, or lots of photos observed from different orientations. Having been authenticated by computer emulation and actual experiment, the results of the proposed technique have proved to be satisfactory. The research has also paved a new way in camera calibration for further studies.

show abstract

Design and Experiment of Cantilevered Lotus Seedpod Picking Device

Zeng,

Xue,

Tang

et al. 2024

Journal of Field Robotics

View full text Add to dashboard Cite

Addressing the issue of the absence of relevant automated harvesting machinery for lotus seedpods, which currently relies solely on manual harvesting, this paper proposes a four‐degree‐of‐freedom cantilever‐type lotus seedpod harvesting device. This harvesting device comprises an electric mobile chassis, mobile mechanism, harvesting mechanism, transfer mechanism, image acquisition system, and control system, which make it suitable for harvesting lotus seedpod in standardized lotus fields. The cantilevered lotus seedpod picking device obtains the image of the lotus from the depth camera at an overlooking angle. The YOLOv5‐trained lotus‐bud recognition model is used to identify and locate the lotus seedpod. Through the calibration using active vision method, the conversion relationship between the ZED camera coordinates and the base coordinates of the picking device is calculated to realize lotus seedpod picking. The results of picking experiments conducted in different time periods show that the picking success rate is higher at night, and in the early morning and evening, with the picking success rates of 89.47%, 85.7%, and 85%, respectively, while the picking success rate at noon is only 42.86%. It can be seen that the ambient light has a great influence on the picking of the lotus seedpods, and it is reasonable to avoid the midday period with strong light for picking. The experimental results show that the clamping coupling method realizes the integration of automatic picking and conveying of lotus seedpods, improves the picking efficiency, and provides a new scheme for fruit picking.

show abstract

A Calibration and 3-D Measurement Method for an Active Vision System With Symmetric Yawing Cameras

Cited by 11 publications

References 20 publications

Simple and High‐Precision Hand–Eye Calibration for 3D Robot Measurement Systems

Simple and High‐Precision Hand–Eye Calibration for 3D Robot Measurement Systems

A New Camera Calibration Technique for Serious Distortion

Design and Experiment of Cantilevered Lotus Seedpod Picking Device

Contact Info

Product

Resources

About