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PIXHAWK: A micro aerial vehicle design for autonomous flight using onboard computer vision

PIXHAWK: A micro aerial vehicle design for autonomous flight using onboard computer vision We describe a novel quadrotor Micro Air Vehicle (MAV) system that is designed to use computer vision algorithms within the flight control loop. The main contribution is a MAV system that is able to run both the vision-based flight control and stereo-vision-based obstacle detection parallelly on an embedded computer onboard the MAV. The system design features the integration of a powerful onboard computer and the synchronization of IMU-Vision measurements by hardware timestamping which allows tight integration of IMU measurements into the computer vision pipeline. We evaluate the accuracy of marker-based visual pose estimation for flight control and demonstrate marker-based autonomous flight including obstacle detection using stereo vision. We also show the benefits of our IMU-Vision synchronization for egomotion estimation in additional experiments where we use the synchronized measurements for pose estimation using the 2pt+gravity formulation of the PnP problem. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Autonomous Robots Springer Journals

PIXHAWK: A micro aerial vehicle design for autonomous flight using onboard computer vision

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References (56)

Publisher
Springer Journals
Copyright
Copyright © 2012 by Springer Science+Business Media, LLC
Subject
Engineering; Artificial Intelligence (incl. Robotics); Control, Robotics, Mechatronics; Computer Imaging, Vision, Pattern Recognition and Graphics; Robotics and Automation
ISSN
0929-5593
eISSN
1573-7527
DOI
10.1007/s10514-012-9281-4
Publisher site
See Article on Publisher Site

Abstract

We describe a novel quadrotor Micro Air Vehicle (MAV) system that is designed to use computer vision algorithms within the flight control loop. The main contribution is a MAV system that is able to run both the vision-based flight control and stereo-vision-based obstacle detection parallelly on an embedded computer onboard the MAV. The system design features the integration of a powerful onboard computer and the synchronization of IMU-Vision measurements by hardware timestamping which allows tight integration of IMU measurements into the computer vision pipeline. We evaluate the accuracy of marker-based visual pose estimation for flight control and demonstrate marker-based autonomous flight including obstacle detection using stereo vision. We also show the benefits of our IMU-Vision synchronization for egomotion estimation in additional experiments where we use the synchronized measurements for pose estimation using the 2pt+gravity formulation of the PnP problem.

Journal

Autonomous Robots – Springer Journals

Published: Feb 23, 2012

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