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

arXiv: https://arxiv.org/abs/1706.03220 · HTML: https://mlsanity.com/paper/1706.03220/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2017.

## Built on by (most cited first, 10 of 10)

- [Colonoscopy Navigation using End-to-End Deep Visuomotor Control: A User Study](https://mlsanity.com/paper/2206.15086/) (2022)
- [ObjectReact: Learning Object-Relative Control for Visual Navigation](https://mlsanity.com/paper/2509.09594/) (2025)
- [CNSv2: Probabilistic Correspondence Encoded Neural Image Servo](https://mlsanity.com/paper/2503.00132/) (2025)
- [Benchmarking Deep Reinforcement Learning Algorithms for Vision-based Robotics](https://mlsanity.com/paper/2201.04224/) (2022)
- [A Closed-Loop Multi-perspective Visual Servoing Approach with Reinforcement Learning](https://mlsanity.com/paper/2312.15809/) (2023)
- [Imitation Learning-based Direct Visual Servoing using the Large Projection Formulation](https://mlsanity.com/paper/2406.09120/) (2024)
- [ODD-SEC: Onboard Drone Detection with a Spinning Event Camera](https://mlsanity.com/paper/2603.06265/) (2026)
- [DiffusionVS: A Generative Framework for Robust Visual Servoing Based on Diffusion Policy](https://mlsanity.com/paper/2606.19397/) (2026)
- [WM-VS: Progress-Aligned World Models for Closed-Loop Visual Servoing](https://mlsanity.com/paper/2609.20892/) (2026)
- [VGM-VS: Rethinking Visual Geometry Model for High-Precision Visual Servoing](https://mlsanity.com/paper/2609.28312/) (2026)

## Abstract (arXiv)

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.