Visual Feedback Control using CNN Based Architecture with Input Data Fusion

Abstract

Visual servoing systems are designed to solve pose alignment problems by providing the necessary linear and angular velocities using data extracted from images. Among the difficulties encountered by the traditional visual servoing approaches, there are feature detection and tracking, camera calibration, scene complexity, and robotic system constraints. Part of these problems can be solved if Convolutional Neural Networks (CNNs) are added to a visual servoing architecture. The main advantage of CNNs is the capability of understanding both the overall structure and specific details of the images corresponding to the current and desired layouts. To take a step further the state-of-the-art architectures, in this paper, we show how extra input data can improve the visual servoing behaviour. The extra data result from maps of regions induced by the feature points' positions, without the necessity of employing tracking. The results obtained on relevant data sets show that the proposed input fusion-based CNN provides an improved approximation of the linear and angular visual servoing velocities.