论文标题
基于学习的摄像机校准框架，具有失真校正和高精度功能检测
Learning-Based Framework for Camera Calibration with Distortion Correction and High Precision Feature Detection
论文作者
论文摘要
摄像机校准是一种至关重要的技术，可显着影响许多机器人系统的性能。鲁棒性和高精度一直是对各种校准方法的追求。然而，基于经典的张方法的最新校准技术仍然受到环境噪声，径向镜头失真和亚最佳参数估计的影响。因此，在本文中，我们提出了一个混合摄像头校准框架，该框架结合了基于学习的方法与传统方法来处理这些瓶颈。特别是，该框架利用基于学习的方法来执行有效的失真校正和强大的棋盘角坐标编码。对于角检测的子像素准确性，提出了具有嵌入异常排斥机制的特殊设计的坐标解码算法。为了避免次优估计结果，我们通过RANSAC算法改善了传统的参数估计，并取得稳定的结果。与两个广泛使用的摄像机校准工具箱相比，对真实和合成数据集的实验都表现出了提出的框架的更好的鲁棒性和更高的精度。庞大的合成数据集是我们框架不错的性能的基础，并将与https://github.com/easonyesheng/ccs公开使用。
Camera calibration is a crucial technique which significantly influences the performance of many robotic systems. Robustness and high precision have always been the pursuit of diverse calibration methods. State-of-the-art calibration techniques based on classical Zhang's method, however, still suffer from environmental noise, radial lens distortion and sub-optimal parameter estimation. Therefore, in this paper, we propose a hybrid camera calibration framework which combines learning-based approaches with traditional methods to handle these bottlenecks. In particular, this framework leverages learning-based approaches to perform efficient distortion correction and robust chessboard corner coordinate encoding. For sub-pixel accuracy of corner detection, a specially-designed coordinate decoding algorithm with embed outlier rejection mechanism is proposed. To avoid sub-optimal estimation results, we improve the traditional parameter estimation by RANSAC algorithm and achieve stable results. Compared with two widely-used camera calibration toolboxes, experiment results on both real and synthetic datasets manifest the better robustness and higher precision of the proposed framework. The massive synthetic dataset is the basis of our framework's decent performance and will be publicly available along with the code at https://github.com/Easonyesheng/CCS.
