METRIC - Multi-Eye To Robot Indoor Calibration Dataset

The METRIC dataset comprises more than 10,000 synthetic and real images of ChAruCo and checkerboard patterns. Each pattern is securely attached to the robot's end-effector, which is systematically moved in front of four cameras surrounding the manipulator. This movement allows for image acquisition from various viewpoints. The real images in the dataset encompass multiple sets of images captured by three distinct types of sensor networks: Microsoft Kinect V2, Intel RealSense Depth D455, and Intel RealSense Lidar L515. The purpose of including these images is to evaluate the advantages and disadvantages of each sensor network for calibration purposes. Additionally, to accurately assess the impact of the distance between the camera and robot on calibration, we obtained a comprehensive synthetic dataset. This dataset contains associated ground truth data and is divided into three different camera network setups, corresponding to three levels of calibration difficulty based on the cell size.METRIC数据集包含超过1万张查鲁科（ChAruCo）棋盘格与普通棋盘格的合成与实拍图像。每类标定板均牢固安装于机器人末端执行器上，系统控制该执行器在环绕机械臂的四台相机前方移动，以此实现多视角图像采集。本数据集的实拍图像涵盖三类不同传感器网络采集的多组图像，分别为微软（Microsoft）Kinect V2、英特尔（Intel）RealSense Depth D455以及英特尔（Intel）RealSense Lidar L515。纳入此类实拍图像的目的在于，针对相机标定任务评估各传感器网络的优劣表现。此外，为精准评估相机与机器人间距对标定效果的影响，我们构建了全面的合成数据集。该数据集包含配套的真值（ground truth）数据，并根据标定板格点尺寸分为三类相机网络配置方案，分别对应三个难度等级的标定任务。