# Visual Servoing from Deep Neural Networks

arXiv: https://arxiv.org/abs/1705.08940 · HTML: https://mlsanity.com/paper/1705.08940/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2017.

## Built on by (most cited first, 2 of 2)

- [Self-supervised Wide Baseline Visual Servoing via 3D Equivariance](https://mlsanity.com/paper/2209.05432/) (2022)
- [Hybrid Visual Servoing of Tendon-driven Continuum Robots](https://mlsanity.com/paper/2502.14092/) (2025)

## Abstract (arXiv)

We present a deep neural network-based method to perform high-precision, robust and real-time 6 DOF visual servoing. The paper describes how to create a dataset simulating various perturbations (occlusions and lighting conditions) from a single real-world image of the scene. A convolutional neural network is fine-tuned using this dataset to estimate the relative pose between two images of the same scene. The output of the network is then employed in a visual servoing control scheme. The method converges robustly even in difficult real-world settings with strong lighting variations and occlusions.A positioning error of less than one millimeter is obtained in experiments with a 6 DOF robot.