We propose novel photogrammetry-based robot calibration methods for industrial robots that are guided by cameras or 3D sensors. Compared to state-of-the-art methods, our methods are capable of calibrating the robot kinematics, the hand–eye transformations, and, for camera-guided robots, the interior orientation of the camera simultaneously. Our approach uses a minimal parameterization of the robot kinematics and hand–eye transformations. Furthermore, it uses a camera model that is capable of handling a large range of complex lens distortions that can occur in cameras that are typically used in machine vision applications. To determine the model parameters, geometrically meaningful photogrammetric error measures are used. They are independent of the parameterization of the model and typically result in a higher accuracy. We apply a stochastic model for all parameters (observations and unknowns), which allows us to assess the precision and significance of the calibrated model parameters. To evaluate our methods, we propose novel procedures that are relevant in real-world applications and do not require ground truth values. Experiments on synthetic and real data show that our approach improves the absolute positioning accuracy of industrial robots significantly. By applying our approach to two different uncalibrated UR3e robots, one guided by a camera and one by a 3D sensor, we were able to reduce the RMS evaluation error by approximately 85% for each robot.

中文翻译：

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使用摄影测量方法进行视觉引导机器人校准


我们为由相机或 3D 传感器引导的工业机器人提出了基于摄影测量的新型机器人标定方法。与最先进的方法相比，我们的方法能够同时校准机器人的运动学、手眼转换，以及相机引导机器人的内部方向。我们的方法使用机器人运动学和手眼转换的最小参数化。此外，它使用的相机型号能够处理机器视觉应用中通常使用的相机中可能发生的大范围复杂镜头畸变。为了确定模型参数，使用了具有几何意义的摄影测量误差度量。它们与模型的参数化无关，通常会产生更高的精度。我们对所有参数（观测值和未知数）应用随机模型，这使我们能够评估校准模型参数的精度和显著性。为了评估我们的方法，我们提出了与实际应用相关且不需要真值的新程序。对合成和真实数据的实验表明，我们的方法显著提高了工业机器人的绝对定位精度。通过将我们的方法应用于两个不同的未校准 UR3e 机器人，一个由摄像头引导，一个由 3D 传感器引导，我们能够将每个机器人的 RMS 评估误差降低约 85%。