# Exploring Convolutional Networks for End-to-End Visual Servoing

## TL;DR

This paper introduces an end-to-end convolutional neural network approach for visual servoing that learns visual features directly from images, enabling robust control in diverse, unstructured environments without prior scene knowledge.

## Contribution

It presents a novel deep learning method for visual servoing that eliminates the need for handcrafted features and prior scene information, applicable in real-world scenarios.

## Key findings

- Effective in simulation and real-world quadrotor tests
- Robust across indoor and outdoor environments
- Handles diverse camera poses without prior scene knowledge

## Abstract

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.