Liu Hongyan, Qu Daokui, Xu Fang, Zou Fengshan, Song Jilai, Jia Kai
Opt Express. 2020 Jun 22;28(13):19058-19073. doi: 10.1364/OE.392414.
RGB-D cameras (or color-depth cameras) play key roles in many vision applications. A typical RGB-D camera has only rough intrinsic and extrinsic calibrations that cannot provide the accuracy required in many vision applications. In this paper, we propose a novel and accurate sphere-based calibration framework for estimating the intrinsic and extrinsic parameters of color-depth sensor pair. Additionally, a method of depth error correction is suggested, and the principle of error correction is analyzed in detail. In our method, the feature extraction module can automatically and reliably detect the center and edges of the sphere projection, while excluding noise data and outliers, and the projection of the sphere center on RGB and depth images is used to obtain a closed solution of the initial parameters. Finally, all the parameters are accurately estimated within the framework of nonlinear global minimization. Compared to other state-of-the-art methods, our calibration method is easy to use and provides higher calibration accuracy. Detailed experimental analysis is performed to support our conclusions.
RGB-D相机（或彩色深度相机）在许多视觉应用中发挥着关键作用。典型的RGB-D相机只有粗略的内参和外参校准，无法满足许多视觉应用所需的精度。在本文中，我们提出了一种新颖且精确的基于球体的校准框架，用于估计彩色深度传感器对的内参和外参。此外，还提出了一种深度误差校正方法，并详细分析了误差校正原理。在我们的方法中，特征提取模块可以自动且可靠地检测球体投影的中心和边缘，同时排除噪声数据和异常值，并利用球体中心在RGB图像和深度图像上的投影来获得初始参数的闭式解。最后，在非线性全局最小化框架内准确估计所有参数。与其他现有方法相比，我们的校准方法易于使用且具有更高的校准精度。进行了详细的实验分析以支持我们的结论。
