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基于激进-保守协同粒子群的双目结构光标定方法综合改进

Comprehensive Improvement of Binocular Structured Light Calibration Method Based on Radical-Conservative Cooperative Particle Swarm.

作者信息

Ma Jun, Meng Xing, Wang Haoseng, Jiang Fangdi, Wang Shifeng, Kodagoda Sarath

机构信息

School of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun 130022, China.

Zhongshan Institute, Changchun University of Science and Technology, Zhongshan 528400, China.

出版信息

Sensors (Basel). 2024 Dec 20;24(24):8155. doi: 10.3390/s24248155.

Abstract

To achieve high-precision 3D reconstruction, a comprehensive improvement has been made to the binocular structured light calibration method. During the calibration process, the calibration object's imaging quality and the camera parameters' nonlinear optimization effect directly affect the caibration accuracy. Firstly, to address the issue of poor imaging quality of the calibration object under tilted conditions, a pixel-level adaptive fill light method was designed using the programmable light intensity feature of the structured light projector, allowing the calibration object to receive uniform lighting and thus improve the quality of the captured images. Then, collaborative Particle Swarm Optimization was studied to optimize the camera parameters. Compared with other optimization algorithms, this algorithm has higher global search capability and can obtain more accurate camera parameters. Under comprehensive improvement, the 3D reconstruction accuracy of binocular structured light is 0.053 mm, showing a 36.33% improvement in reconstruction accuracy compared to mainstream calibration methods.

摘要

为实现高精度三维重建,对双目结构光标定方法进行了全面改进。在标定过程中,标定物体的成像质量和相机参数的非线性优化效果直接影响标定精度。首先,针对标定物体在倾斜条件下成像质量差的问题,利用结构光投影仪的可编程光强特性设计了一种像素级自适应补光方法,使标定物体能够获得均匀照明,从而提高采集图像的质量。然后,研究了协同粒子群优化算法来优化相机参数。与其他优化算法相比,该算法具有更高的全局搜索能力,能够获得更精确的相机参数。经过全面改进后,双目结构光的三维重建精度达到0.053毫米,与主流标定方法相比,重建精度提高了36.33%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/124e/11679909/27ceb3abb341/sensors-24-08155-g001.jpg

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