Visual Servoing from Deep Neural Networks

2017/05/24 by Quentin Bateux, Éric Marchand, Bateux, Quentin +7
Computer Science · Engineering · #Advanced Vision and Imaging #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image Processing Techniques and Applications #Robotics (cs.RO) #Robotics and Sensor-Based Localization

paper · pdf · doi:10.48550/arxiv.1705.08940

openalex publication_date 2017/05/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• LabelMe: A Database and Web-Based Tool for Image Annotation
• Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control
• Deep Image Homography Estimation
• Caffe: Convolutional Architecture for Fast Feature Embedding

Related

• DFVS: Deep Flow Guided Scene Agnostic Image Based Visual Servoing
• Exploring Convolutional Networks for End-to-End Visual Servoing
• Siamese Convolutional Neural Network for Sub-millimeter-accurate Camera Pose Estimation and Visual Servoing
• Visual Servoing with Geometrically Interpretable Neural Perception
• DFBVS: Deep Feature-Based Visual Servo