English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Nonlinear ego-motion estimation from optical flow for online control of a quadrotor UAV

Grabe, V., Bülthoff, H., Scaramuzza, D., & Robuffo Giordano, P. (2015). Nonlinear ego-motion estimation from optical flow for online control of a quadrotor UAV. International Journal of Robotics Research, 34(8), 1114-1135. doi:10.1177/0278364915578646.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002A-4578-7 Version Permalink: http://hdl.handle.net/21.11116/0000-0000-B666-0
Genre: Journal Article

Files

show Files

Locators

show
hide
Description:
-

Creators

show
hide
 Creators:
Grabe, V1, Author              
Bülthoff, HH1, Author              
Scaramuzza, D, Author
Robuffo Giordano, P1, Author              
Affiliations:
1Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497797              

Content

show
hide
Free keywords: -
 Abstract: For the control of unmanned aerial vehicles (UAVs) in GPS-denied environments, cameras have been widely exploited as the main sensory modality for addressing the UAV state estimation problem. However, the use of visual information for ego-motion estimation presents several theoretical and practical difficulties, such as data association, occlusions, and lack of direct metric information when exploiting monocular cameras. In this paper, we address these issues by considering a quadrotor UAV equipped with an onboard monocular camera and an inertial measurement unit (IMU). First, we propose a robust ego-motion estimation algorithm for recovering the UAV scaled linear velocity and angular velocity from optical flow by exploiting the so-called continuous homography constraint in the presence of planar scenes. Then, we address the problem of retrieving the (unknown) metric scale by fusing the visual information with measurements from the onboard IMU. To this end, two different estimation strategies are proposed and critically compared: a first exploiting the classical extended Kalman filter (EKF) formulation, and a second one based on a novel nonlinear estimation framework. The main advantage of the latter scheme lies in the possibility of imposing a desired transient response to the estimation error when the camera moves with a constant acceleration norm with respect to the observed plane. We indeed show that, when compared against the EKF on the same trajectory and sensory data, the nonlinear scheme yields considerably superior performance in terms of convergence rate and predictability of the estimation. The paper is then concluded by an extensive experimental validation, including an onboard closed-loop control of a real quadrotor UAV meant to demonstrate the robustness of our approach in real-world conditions.

Details

show
hide
Language(s):
 Dates: 2015-07
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1177/0278364915578646
BibTex Citekey: GrabeBSR2015
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: International Journal of Robotics Research
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 34 (8) Sequence Number: - Start / End Page: 1114 - 1135 Identifier: -