Motion control and data capturing for laser scanning with an industrial robot

Larsson, Sofia; Kjellander, Johan

doi:10.1016/j.robot.2006.02.002

Cited by 52 publications

(26 citation statements)

References 8 publications

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“…Wu D F [10]- [11] , Giovanna Sansoni [12]- [13] robot + Laser Scanner + camera mouse, kettle, etc. Soren Larsson, Mohanmed R. Rahayem [14]- [15] robot + structure light sensor + camera car model, motorcycle fuel tank Gong C H [16]- [17] Zhu J H [18] Research results show that industrial robots can be used as carrier driving element measurement system and measurement equipment, for the practical application of these combined measurement system needs further research, the key technology in three aspects: system calibration, measurement planning and data processing [5] . System calibration refers to components in the measurement system the mechanism, the calibration accuracy directly affects the final accuracy of the measurement system, the calibration of general quasi order: robot calibration, sensor calibration, calibration of robot and sensor location.…”

Section: Combined Measurement Technology Based On Robotmentioning

confidence: 99%

Summary of the Space Coordinate Measuring Technology Via Robots

Li¹

2017

IOSR JMCE

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Section: Combined Measurement Technology Based On Robotmentioning

confidence: 99%

Summary of the Space Coordinate Measuring Technology Via Robots

Li¹

2017

IOSR JMCE

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“…There are several approaches for the robot-based scanning of objects [5], [6], [7], [8]. In these projects, the resulting scans are used for a variety of applications, such as processing, reverse engineering, inspection or design.…”

Section: A Robot-based Object Scanningmentioning

confidence: 99%

“…In an automatic approach, a system for the generation of the paths needs to be developed. Larsson and Kjellander [5] created the paths for the data capturing based on CAD curves, for example. Borangiu and Dumitrache [6] presented a simulation platform for the scanning process, including a robot motion simulation and an optical simulation of the laser sensor.…”

Section: A Robot-based Object Scanningmentioning

confidence: 99%

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Self-calibration method for a robotic based 3D scanning system

Wagner

Heb

Reitelshöfer

et al. 2015

2015 IEEE 20th Conference on Emerging Technologies &Amp; Factory Automation (ETFA)

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This paper describes a method for extrinsic sensor calibration for a 2D laser profile sensor on a robot arm used as a robot based 3D scanning system. In order to establish a relationship between the sensor measurements and the robot positions, the transformation between the robot flange and the sensor reference frame are determined. The developed calibration method is implemented as an automated self-calibration. It is based on the use of a pin as a fixed reference. The robot is aligned to the tip of the pin automatically with certain orientations. The sensor transformation is calculated based on six recorded robot positions. Finally, the accuracy of the calibration is determined with an additional robot position.

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“…The image from a laser line shining on the object is the source of information to achieve the measurement task. One of the key steps in the whole measurement task is to extract accurately the center positions of a laser line [9,10]. The ideal center positions of a laser line should be a singlepixel-wide curve located in the center of the light line [11,12].…”

mentioning

confidence: 99%

Adaptable Center Detection of a Laser Line with a Normalization Approach using Hessian-matrix Eigenvalues

Xu¹,

Sun²,

Li³

et al. 2014

Journal of the Optical Society of Korea

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In vision measurement systems based on structured light, the key point of detection precision is to determine accurately the central position of the projected laser line in the image. The purpose of this research is to extract laser line centers based on a decision function generated to distinguish the real centers from candidate points with a high recognition rate. First, preprocessing of an image adopting a difference image method is conducted to realize image segmentation of the laser line. Second, the feature points in an integral pixel level are selected as the initiating light line centers by the eigenvalues of the Hessian matrix. Third, according to the light intensity distribution of a laser line obeying a Gaussian distribution in transverse section and a constant distribution in longitudinal section, a normalized model of Hessian matrix eigenvalues for the candidate centers of the laser line is presented to balance reasonably the two eigenvalues that indicate the variation tendencies of the second-order partial derivatives of the Gaussian function and constant function, respectively. The proposed model integrates a Gaussian recognition function and a sinusoidal recognition function. The Gaussian recognition function estimates the characteristic that one eigenvalue approaches zero, and enhances the sensitivity of the decision function to that characteristic, which corresponds to the longitudinal direction of the laser line. The sinusoidal recognition function evaluates the feature that the other eigenvalue is negative with a large absolute value, making the decision function more sensitive to that feature, which is related to the transverse direction of the laser line. In the proposed model the decision function is weighted for higher values to the real centers synthetically, considering the properties in the longitudinal and transverse directions of the laser line. Moreover, this method provides a decision value from 0 to 1 for arbitrary candidate centers, which yields a normalized measure for different laser lines in different images. The normalized results of pixels close to 1 are determined to be the real centers by progressive scanning of the image columns. Finally, the zero point of a second-order Taylor expansion in the eigenvector's direction is employed to refine further the extraction results of the central points at the subpixel level. The experimental results show that the method based on this normalization model accurately extracts the coordinates of laser line centers and obtains a higher recognition rate in two group experiments.

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Motion control and data capturing for laser scanning with an industrial robot

Cited by 52 publications

References 8 publications

Summary of the Space Coordinate Measuring Technology Via Robots

Summary of the Space Coordinate Measuring Technology Via Robots

Self-calibration method for a robotic based 3D scanning system

Adaptable Center Detection of a Laser Line with a Normalization Approach using Hessian-matrix Eigenvalues

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