Research on the Optimization Method of Visual Sensor Calibration Combining Convex Lens Imaging with the Bionic Algorithm of Wolf Pack Predation

To improve the accuracy of camera calibration, a novel optimization method is proposed in this paper, which combines convex lens imaging with the bionic algorithm of Wolf Pack Predation (CLI-WPP). During the optimization process, the internal parameters and radial distortion parameters of the camera...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 24; no. 18; p. 5926
Main Authors: Wu, Qingdong, Miao, Jijun, Liu, Zhaohui, Chang, Jiaxiu
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 12.09.2024
MDPI
Subjects:
Accuracy
Algorithms
Calibration
camera calibration
Cameras
CLI-WPP
Efficiency
Learning strategies
Mathematical optimization
Methods
optimization algorithm
Optimization algorithms
Predation (Biology)
reprojection error
reverse learning strategy
Vision systems
optimization algorithm
reverse learning strategy
CLI-WPP
camera calibration
reprojection error
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:To improve the accuracy of camera calibration, a novel optimization method is proposed in this paper, which combines convex lens imaging with the bionic algorithm of Wolf Pack Predation (CLI-WPP). During the optimization process, the internal parameters and radial distortion parameters of the camera are regarded as the search targets of the bionic algorithm of Wolf Pack Predation, and the reprojection error of the calibration results is used as the fitness evaluation criterion of the bionic algorithm of Wolf Pack Predation. The goal of optimizing camera calibration parameters is achieved by iteratively searching for a solution that minimizes the fitness value. To overcome the drawback that the bionic algorithm of Wolf Pack Predation is prone to fall into local optimal, a reverse learning strategy based on convex lens imaging is introduced to transform the current optimal individual and generate a series of new individuals with potential better solutions that are different from the original individual, helping the algorithm out of the local optimum dilemma. The comparative experimental results show that the average reprojection errors of the simulated annealing algorithm, Zhang’s calibration method, the sparrow search algorithm, the particle swarm optimization algorithm, bionic algorithm of Wolf Pack Predation, and the algorithm proposed in this paper (CLI-WPP) are 0.42986500, 0.28847656, 0.23543161, 0.219342495, 0.10637477, and 0.06615037, respectively. The results indicate that calibration accuracy, stability, and robustness are significantly improved with the optimization method based on the CLI-WPP, in comparison to the existing commonly used optimization algorithms.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s24185926