The 3D measurement system based on line-structured light uses a camera to capture laser stripes due to changing in the shape of an object, and uses the acquired pixel coordinates for 3D reconstruction. System calibration is an important step in 3D measurement. The current camera calibration algorithm research mainly focuses on improving the algorithm itself, and there is less research on the influence of external factors. This paper proposes a coplanar hybrid calibration algorithm based on the error screening model by combining the error screening model, mathematical model and neural network model. It is mainly divided into two steps. The first step is to use the radial array constraint calibration algorithm based on the error screening model to solve the camera’s internal and external parameters. The second step uses the camera internal and external parameters obtained in the first step to convert the pixel coordinates into real three-dimensional coordinates, and compares the calculated three-dimensional coordinates with the actual coordinates. Using machine learning to establish a compensation network, get a compensation function, and use the resulting 3D world coordinates to perform point cloud stitching. Experiments show that compared with the traditional calibration algorithm, the calibration algorithm has a small error and reduces the calibration error by about 6.5%.

中文翻译：

---

基于线结构光的误差筛选标定算法

基于线结构光的3D测量系统利用摄像头捕捉物体形状变化引起的激光条纹，并利用获取的像素坐标进行3D重建。系统校准是 3D 测量的重要步骤。目前相机标定算法研究主要集中在算法本身的改进上，对外界因素影响的研究较少。本文结合误差筛选模型、数学模型和神经网络模型，提出了一种基于误差筛选模型的共面混合标定算法。主要分为两步。第一步是使用基于误差筛选模型的径向阵列约束标定算法求解相机的内外参数。第二步，利用第一步得到的相机内外参数，将像素坐标转换为真实的三维坐标，并将计算得到的三维坐标与实际坐标进行比较。利用机器学习建立补偿网络，得到补偿函数，利用得到的3D世界坐标进行点云拼接。实验表明，与传统标定算法相比，该标定算法误差较小，标定误差降低约6.5%。得到一个补偿函数，并使用得到的 3D 世界坐标进行点云拼接。实验表明，与传统标定算法相比，该标定算法误差较小，标定误差降低约6.5%。得到一个补偿函数，并使用得到的 3D 世界坐标进行点云拼接。实验表明，与传统标定算法相比，该标定算法误差较小，标定误差降低约6.5%。