Robust Intrinsic and Extrinsic Calibration of RGB-D Cameras

Color-depth cameras (RGB-D cameras) have become the primary sensors in most robotics systems, from service robotics to industrial robotics applications. Typical consumergrade RGB-D cameras are provided with a coarse intrinsic and extrinsic calibration that generally does not meet the accuracy requirements needed by many robotics applications (e.g., high accuracy 3D environment reconstruction and mapping, high precision object recognition and localization, . . . ) In this paper, we propose a human-friendly, reliable and accurate calibration framework that enables to easily estimate both the intrinsic and extrinsic parameters of a general color-depth sensor couple. Our approach is based on a novel, two components, measurement error model that unifies the error sources of different RGB-D pairs based on different technologies, such as structuredlight 3D cameras and time-of-flight cameras. The proposed correction model is implemented using two different parametric undistortion maps that provide the calibrated readings by means of linear combinations of control functions. Our method provides some important advantages compared to other state-of-the-art systems: it is general (i.e., well suited for different types of sensors), it is based on an easy and stable calibration protocol, it provides a greater calibration accuracy, and it has been implemented within the ROS robotics framework. We report detailed and comprehensive experimental validations and performance comparisons to support our statements.