Close-quarters Quadrotor flying for a pole inspection with position based visual servoing and high-speed vision

Sa, Inkyu & Corke, Peter (2014) Close-quarters Quadrotor flying for a pole inspection with position based visual servoing and high-speed vision. In Proceedings of the 2014 International Conference on Unmanned Aircraft Systems (ICUAS), IEEE, Orlando, Florida, The United States of America, pp. 623-631.

View at publisher

Abstract

This paper presents a 100 Hz monocular position based visual servoing system to control a quadrotor flying in close proximity to vertical structures approximating a narrow, locally linear shape. Assuming the object boundaries are represented by parallel vertical lines in the image, detection and tracking is achieved using Plücker line representation and a line tracker. The visual information is fused with IMU data in an EKF framework to provide fast and accurate state estimation. A nested control design provides position and velocity control with respect to the object. Our approach is aimed at high performance on-board control for applications allowing only small error margins and without a motion capture system, as required for real world infrastructure inspection. Simulated and ground-truthed experimental results are presented.

Impact and interest:

0 citations in Scopus
Search Google Scholar™
1 citations in Web of Science®

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 74394
Item Type: Conference Paper
Refereed: Yes
Keywords: Cameras, Computational modeling, Estimation, Mathematical model, Sensors, Vectors, Vehciles
DOI: 10.1109/ICUAS.2014.6842306
ISBN: 9781479923762
Divisions: Current > Research Centres > ARC Centre of Excellence for Robotic Vision
Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 IEEE
Deposited On: 24 Jul 2014 22:58
Last Modified: 28 Jul 2014 00:19

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page