Hongyu Lv, Maoyue Li, Yuanqiang Su, Chenglong Zhang, Jingzhi Xu
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引用次数: 0
Abstract
This paper presents a novel method for the intelligent adjustment of vision parameters in structured light camera calibration under complex light conditions, aiming to enhance accuracy and reduce interference from human and external factors. Firstly, a self-learning weight calibration feature extraction model (SLWFE model) is developed to solve the coupled interference problem of calibration feature extraction. Secondly, we analyze the influence of focal length on structured light phase-height mapping accuracy and construct a grating calibration characteristic gradient filter function. The focus confidence evaluation model of calibration image is proposed, to realize the accurate calculation of optimal exposure time and lens ideal focus position, leading to the development of the grating calibration image characteristics optimization algorithm (GCICO). Finally, an intelligent parameterization device and control system were created, integrating the algorithm for experimental verification, achieving an average reprojection error of 0.018 pixels and an improvement of 70.49% over traditional methods.
期刊介绍:
Contributions are invited on novel achievements in all fields of measurement and instrumentation science and technology. Authors are encouraged to submit novel material, whose ultimate goal is an advancement in the state of the art of: measurement and metrology fundamentals, sensors, measurement instruments, measurement and estimation techniques, measurement data processing and fusion algorithms, evaluation procedures and methodologies for plants and industrial processes, performance analysis of systems, processes and algorithms, mathematical models for measurement-oriented purposes, distributed measurement systems in a connected world.