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. Visual servoing uses computer vision algorithms to guide robot movements. 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 for 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 that rely only on the information extracted from images. Furthermore, end-to-end systems eliminate entirely the controller by learning the control laws during training.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 within robotics.

In recent years, the development of ground robots with human-like perception capabilities has led to the use of multiple sensors, including cameras, lidars, and radars, along with deep learning techniques for detecting and recognizing objects and estimating distances. This paper proposes a computer vision-based navigation system that integrates object detection, segmentation, and monocular depth estimation using deep neural networks to identify predefined target objects and navigate towards them with a single monocular camera as a sensor. Our experiments include different sensitivity analyses to evaluate the impact of monocular cues on distance estimation. We show that this system can provide a ground robot with the perception capabilities needed for autonomous navigation in unknown indoor environments without the need for prior mapping or external positioning systems. This technique provides an efficient and cost-effective means of navigation, overcoming the limitations of other navigation techniques such as GPS-based and SLAM-based navigation. Graphical Abstract