Self-calibration of colormetric parameters in vision systems for autonomous mobile robots

Vision is an extremely important sense for both humans and robots, providing detailed information about the environment. In the past few years, the use of digital cameras in robotic applications has been increasing significantly. The use of digital cameras as the main sensor allows the robot to take the relevant information from the surrounding environment and then take decisions. A robust vision system should be able to detect objects reliably and present an accurate representation of the world to higherlevel processes, not only under ideal light conditions, but also under changing light intensity and color balance. In this paper, we propose an algorithm for the self-calibration of the most important parameters of digital cameras for robotic applications. The algorithm extracts statistical information from the acquired images, namely the intensity histogram, saturation histogram and information from a black and a white area of the image, to then estimate the colormetric parameters of the camera. We present experimental results obtained with several autonomous robotic platforms: two wheeled platforms, with different architectures of the vision system, a humanoid robots and an autonomous driving agent. The images acquired after calibration show good properties for further processing, independently of the initial configuration of the camera and the type and amount of light of the environment, both indoor and outdoor.