Classical and Deep Learning based Visual Servoing Systems: A Survey on State of the Art

Computer vision, together with bayesian estimation algorithms, sensors, and actuators, are used in robotics to solve a variety of critical tasks such as localization, obstacle avoidance, and navigation. Classical approaches in visual servoing systems relied on extracting features from images to control robot movements. Now, state of the art computer vision systems use deep neural networks in tasks such as object recognition, detection, segmentation, and tracking. These networks and specialized controllers play a predominant role in the design and implementation of modern visual servoing systems due to their accuracy, flexibility, and adaptability. Recent research in direct systems for visual servoing has created robotic systems capable of relying only on the information contained in the whole image. Furthermore, end-to-end systems learn the control laws during training, eliminating entirely the controller. This paper presents a comprehensive survey on the state of the art in visual servoing systems, discussing the latest classical methods not included in other surveys but emphasizing the new approaches based on deep neural networks and their applications in a broad variety of applications within robotics.