Exploring Convolutional Networks for End-to-End Visual Servoing
This addresses the challenge of automating robotic control in unknown environments for applications like drones, though it is incremental as it adapts existing deep learning methods to a specific task.
The paper tackles the problem of visual servoing in unstructured environments without prior knowledge of camera parameters or scene geometry by training a convolutional neural network on color images with synchronized camera poses, demonstrating efficacy and robustness in simulation and on a quadrotor across indoor and outdoor settings.
Present image based visual servoing approaches rely on extracting hand crafted visual features from an image. Choosing the right set of features is important as it directly affects the performance of any approach. Motivated by recent breakthroughs in performance of data driven methods on recognition and localization tasks, we aim to learn visual feature representations suitable for servoing tasks in unstructured and unknown environments. In this paper, we present an end-to-end learning based approach for visual servoing in diverse scenes where the knowledge of camera parameters and scene geometry is not available a priori. This is achieved by training a convolutional neural network over color images with synchronised camera poses. Through experiments performed in simulation and on a quadrotor, we demonstrate the efficacy and robustness of our approach for a wide range of camera poses in both indoor as well as outdoor environments.