Institute of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Beijing Key Laboratory: Measurement and Control of Mechanical and Electrical System, Beijing Information Science and Technology University, Beijing 100192, China.
Sensors (Basel). 2018 Nov 15;18(11):3964. doi: 10.3390/s18113964.
Highly accurate and easy-to-operate calibration (to determine the interior and distortion parameters) and orientation (to determine the exterior parameters) methods for cameras in large volume is a very important topic for expanding the application scope of 3D vision and photogrammetry techniques. This paper proposes a method for simultaneously calibrating, orienting and assessing multi-camera 3D measurement systems in large measurement volume scenarios. The primary idea is building 3D point and length arrays by moving a scale bar in the measurement volume and then conducting a self-calibrating bundle adjustment that involves all the image points and lengths of both cameras. Relative exterior parameters between the camera pair are estimated by the five point relative orientation method. The interior, distortion parameters of each camera and the relative exterior parameters are optimized through bundle adjustment of the network geometry that is strengthened through applying the distance constraints. This method provides both internal precision and external accuracy assessment of the calibration performance. Simulations and real data experiments are designed and conducted to validate the effectivity of the method and analyze its performance under different network geometries. The RMSE of length measurement is less than 0.25 mm and the relative precision is higher than 1/25,000 for a two camera system calibrated by the proposed method in a volume of 12 m × 8 m × 4 m. Compared with the state-of-the-art point array self-calibrating bundle adjustment method, the proposed method is easier to operate and can significantly reduce systematic errors caused by wrong scaling.
在大体积中对相机进行高精度且易于操作的校准(确定内部和失真参数)和定向(确定外部参数)是扩展 3D 视觉和摄影测量技术应用范围的一个非常重要的课题。本文提出了一种在大测量体积场景中同时校准、定向和评估多相机 3D 测量系统的方法。主要思想是通过在测量体积中移动比例尺构建 3D 点和长度阵列,然后对涉及所有图像点和两个相机的长度的自校准捆绑调整进行处理。通过五点相对定向方法估计相机对之间的相对外部参数。通过应用距离约束来增强网络几何结构的捆绑调整,对每个相机的内部、失真参数和相对外部参数进行优化。该方法提供了校准性能的内部精度和外部准确性评估。设计并进行了模拟和真实数据实验,以验证该方法的有效性,并分析不同网络几何结构下的性能。在 12 m×8 m×4 m 的体积中,用所提出的方法对两个相机系统进行校准后,长度测量的均方根误差(RMSE)小于 0.25mm,相对精度高于 1/25000。与最先进的点阵列自校准捆绑调整方法相比,所提出的方法更易于操作,并且可以显著减少因错误缩放而引起的系统误差。