A High Precision Approach to Calibrate a Structured Light Vision Sensor in a Robot-Based Three-Dimensional Measurement System
A robot-based three-dimensional (3D) measurement system is presented. In the presented system, a structured light vision sensor is mounted on the arm of an industrial robot. Measurement accuracy is one of the most important aspects of any 3D measurement system. To improve the measuring accuracy of t...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-08-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/16/9/1388 |
| _version_ | 1817989915588165632 |
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| author | Defeng Wu Tianfei Chen Aiguo Li |
| author_facet | Defeng Wu Tianfei Chen Aiguo Li |
| author_sort | Defeng Wu |
| collection | DOAJ |
| description | A robot-based three-dimensional (3D) measurement system is presented. In the presented system, a structured light vision sensor is mounted on the arm of an industrial robot. Measurement accuracy is one of the most important aspects of any 3D measurement system. To improve the measuring accuracy of the structured light vision sensor, a novel sensor calibration approach is proposed to improve the calibration accuracy. The approach is based on a number of fixed concentric circles manufactured in a calibration target. The concentric circle is employed to determine the real projected centres of the circles. Then, a calibration point generation procedure is used with the help of the calibrated robot. When enough calibration points are ready, the radial alignment constraint (RAC) method is adopted to calibrate the camera model. A multilayer perceptron neural network (MLPNN) is then employed to identify the calibration residuals after the application of the RAC method. Therefore, the hybrid pinhole model and the MLPNN are used to represent the real camera model. Using a standard ball to validate the effectiveness of the presented technique, the experimental results demonstrate that the proposed novel calibration approach can achieve a highly accurate model of the structured light vision sensor. |
| first_indexed | 2024-04-14T00:52:16Z |
| format | Article |
| id | doaj.art-0a2b7cf58e65485b8160723facf978b0 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T00:52:16Z |
| publishDate | 2016-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0a2b7cf58e65485b8160723facf978b02022-12-22T02:21:43ZengMDPI AGSensors1424-82202016-08-01169138810.3390/s16091388s16091388A High Precision Approach to Calibrate a Structured Light Vision Sensor in a Robot-Based Three-Dimensional Measurement SystemDefeng Wu0Tianfei Chen1Aiguo Li2School of Marine Engineering, Jimei University, Xiamen 361021, ChinaSchool of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, ChinaSchool of Information Science and Technology, Dalian Maritime University, Dalian 116026, ChinaA robot-based three-dimensional (3D) measurement system is presented. In the presented system, a structured light vision sensor is mounted on the arm of an industrial robot. Measurement accuracy is one of the most important aspects of any 3D measurement system. To improve the measuring accuracy of the structured light vision sensor, a novel sensor calibration approach is proposed to improve the calibration accuracy. The approach is based on a number of fixed concentric circles manufactured in a calibration target. The concentric circle is employed to determine the real projected centres of the circles. Then, a calibration point generation procedure is used with the help of the calibrated robot. When enough calibration points are ready, the radial alignment constraint (RAC) method is adopted to calibrate the camera model. A multilayer perceptron neural network (MLPNN) is then employed to identify the calibration residuals after the application of the RAC method. Therefore, the hybrid pinhole model and the MLPNN are used to represent the real camera model. Using a standard ball to validate the effectiveness of the presented technique, the experimental results demonstrate that the proposed novel calibration approach can achieve a highly accurate model of the structured light vision sensor.http://www.mdpi.com/1424-8220/16/9/1388robot based 3D measurement systemMLPNNstructured light vision sensor calibrationconcentric circle |
| spellingShingle | Defeng Wu Tianfei Chen Aiguo Li A High Precision Approach to Calibrate a Structured Light Vision Sensor in a Robot-Based Three-Dimensional Measurement System Sensors robot based 3D measurement system MLPNN structured light vision sensor calibration concentric circle |
| title | A High Precision Approach to Calibrate a Structured Light Vision Sensor in a Robot-Based Three-Dimensional Measurement System |
| title_full | A High Precision Approach to Calibrate a Structured Light Vision Sensor in a Robot-Based Three-Dimensional Measurement System |
| title_fullStr | A High Precision Approach to Calibrate a Structured Light Vision Sensor in a Robot-Based Three-Dimensional Measurement System |
| title_full_unstemmed | A High Precision Approach to Calibrate a Structured Light Vision Sensor in a Robot-Based Three-Dimensional Measurement System |
| title_short | A High Precision Approach to Calibrate a Structured Light Vision Sensor in a Robot-Based Three-Dimensional Measurement System |
| title_sort | high precision approach to calibrate a structured light vision sensor in a robot based three dimensional measurement system |
| topic | robot based 3D measurement system MLPNN structured light vision sensor calibration concentric circle |
| url | http://www.mdpi.com/1424-8220/16/9/1388 |
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