基于强化学习的激光三角光斑定位方法研究

Research on laser triangular spot localization method based on reinforcement learning

光斑定位过程中,若激光三角光斑图像质量较差,会直接影响后续光斑定位精度,为此提出基于强化学习的激光三角光斑定位方法。采集激光三角光斑图像,并对采集得到的图像展开去噪分割等处理,去除图像噪声,获取光斑目标,提升图像质量;引入邻域标定光斑对处理后的图像实施互相关运算,充分利用光斑的偏态分布灰度以及位置的相似性约束,确定目标像素级的相关系数分布。对获取的相关系数分布结果实施三次非均匀有理B样条插值细分,根据细分结果完成光斑中心定位方程的建立,利用强化学习算法对定位方程实施优化处理,进一步提升定位精度,实现对激光三角光斑的精准定位。实验结果表明,利用该方法开展激光三角光斑定位时,定位效果好、精度高。

In the process of spot localization,if the quality of the laser triangular spot image is poor,it will directly affect the accuracy of subsequent spot localization.Therefore,a laser triangular spot localization method based on reinforcement learning is proposed.Collect laser triangular spot images and perform denoising and segmentation on the collected images to remove image noise,obtain spot targets,and improve image quality;Introducing neighborhood calibration light spots to perform cross correlation operations on the processed image,fully utilizing the skewed distribution of light spots and the similarity constraints of their positions,to determine the distribution of correlation coefficients at the target pixel level.Implement cubic non-uniform rational B-spline interpolation subdivision on the obtained correlation coefficient distribution results,establish the center positioning equation of the laser spot based on the subdivision results,optimize the positioning equation using reinforcement learning algorithm,further improve the positioning accuracy,and achieve precise positioning of the laser three corner spot.The experimental results show that using this method for laser triangulation spot positioning has good positioning effect and high accuracy.