Virtual binocular vision systems to solid model reconstruction

Chen, Chieh‐Li; Tai, Chung-Li; Lio, Y. F.

doi:10.1007/s00170-007-1178-6

Cited by 16 publications

(2 citation statements)

References 11 publications

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“…In recent years, due to the rapid development of this technology, robot navigation, visual detection, artificial intelligence and other industries have also changed with significant breakthroughs. Binocular vision is different from monocular vision in that it can obtain the depth information of the object according to different positions in the object space [4]- [6]. In the process of actual 3D measurement, it has the advantages of high efficiency and accuracy.…”

Section: Introductionmentioning

confidence: 99%

Research on the Calibration of Binocular Camera Based on BP Neural Network Optimized by Improved Genetic Simulated Annealing Algorithm

2020

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The Back Propagation (BP) neural network has the problems of low accuracy and poor convergence in the process of binocular camera calibration. A method based on BP neural network optimized by improved genetic simulated annealing algorithm (IGSAA-BP) is proposed to solve these problems to complete the binocular camera calibration. The method of combining Gaussian scale space and Harris corner detection operator is used for corner detection. A matched algorithm of homonymous corner is proposed by combining point-to-point spatial mapping and grid motion statistics. The pixel values of the homonymous corner and three-dimensional coordinate values are taken as the input and output of BP neural network respectively. The crossover and mutation probability of genetic simulated annealing algorithm and the annealing criterion are improved, the IGSAA-BP neural network is used to calibrate the binocular camera. The average calibration accuracy of BP neural network and IGSAA-BP neural network is 0.71mm and 0.03mm, respectively. The average calibration accuracy of binocular camera is improved by 96%. The iteration speed is increased by 20 times and global optimization ability is improved. It can be seen that the IGSAA-BP neural network can improve the calibration accuracy of binocular camera and accelerate convergence speed.INDEX TERMS Camera calibration, genetic simulated annealing algorithm, Gaussian scale space, corner detection, homonymous corner match, BP neural network.

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Section: Introductionmentioning

confidence: 99%

Research on the Calibration of Binocular Camera Based on BP Neural Network Optimized by Improved Genetic Simulated Annealing Algorithm

2020

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“…However, the calibration accuracy is not stable due to the limitation of the number of feature points. Chen et al 18 calibrated a virtual binocular vision system with coordinates of feature points which can be determined through matching procedure and searching. The method is cost efficient but too complicated.…”

Section: Introductionmentioning

confidence: 99%

Flexible method for accurate calibration of large-scale vision metrology system based on virtual 3-D targets and laser tracker

Zhang

et al. 2019

International Journal of Advanced Robotic Systems

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For the purpose of obtaining high-precision in stereo vision calibration, a large-size precise calibration target, which can cover more than half of the field of view is vital. However, large-scale calibration targets are very difficult to fabricate. Based on the idea of error tracing, a high-precision calibration method for vision system with large field of view by constructing a virtual 3-D calibration target with a laser tracker was proposed in this article. A virtual 3-D calibration target that covers the whole measurement space can be established flexibly and the measurement precision of the vision system can be traceable to the laser tracker. First, virtual 3-D targets by calculating rigid body transformation with unit quaternion method were constructed. Then, the high-order distortion camera model was taken into consideration. Besides, the calibration parameters were solved with Levenberg–Marquardt optimization algorithm. In the experiment, a binocular stereo vision system with the field of view of 4 × 3 × 2 m3 was built for verifying the validity and precision of the proposed calibration method. It is measured that the accuracy with the proposed method can be greatly improved comparing with traditional plane calibration method. The method can be widely used in industrial applications, such as in the field of calibrating large-scale vision-based coordinate metrology, and six-degrees of freedom pose tracking system for dimensional measurement of workpiece, as well as robotics geometrical accuracy detection and compensation.

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Research on Obstacle Avoidance of Mobile Robot Based on Multi-sensor Fusion

Wang

Guan

2019

Advances in Intelligent Systems and Computing

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Virtual binocular vision systems to solid model reconstruction

Cited by 16 publications

References 11 publications

Research on the Calibration of Binocular Camera Based on BP Neural Network Optimized by Improved Genetic Simulated Annealing Algorithm

Research on the Calibration of Binocular Camera Based on BP Neural Network Optimized by Improved Genetic Simulated Annealing Algorithm

Flexible method for accurate calibration of large-scale vision metrology system based on virtual 3-D targets and laser tracker

Research on Obstacle Avoidance of Mobile Robot Based on Multi-sensor Fusion

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