Next Article in Journal
Ubiquitous Creation of Bas-Relief Surfaces with Depth-of-Field Effects Using Smartphones
Next Article in Special Issue
Coastal Areas Division and Coverage with Multiple UAVs for Remote Sensing
Previous Article in Journal
MEMS and FOG Technologies for Tactical and Navigation Grade Inertial Sensors—Recent Improvements and Comparison
Previous Article in Special Issue
A Novel System for Correction of Relative Angular Displacement between Airborne Platform and UAV in Target Localization
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(3), 571; doi:10.3390/s17030571

Time-of-Travel Methods for Measuring Optical Flow on Board a Micro Flying Robot

Aix-Marseille Université, CNRS, ISM UMR7287, 13288 Marseille Cedex 09, France
*
Author to whom correspondence should be addressed.
Academic Editors: Felipe Gonzalez Toro and Antonios Tsourdos
Received: 30 December 2016 / Revised: 8 March 2017 / Accepted: 9 March 2017 / Published: 11 March 2017
(This article belongs to the Special Issue UAV-Based Remote Sensing)
View Full-Text   |   Download PDF [9104 KB, uploaded 14 March 2017]   |  

Abstract

For use in autonomous micro air vehicles, visual sensors must not only be small, lightweight and insensitive to light variations; on-board autopilots also require fast and accurate optical flow measurements over a wide range of speeds. Using an auto-adaptive bio-inspired Michaelis–Menten Auto-adaptive Pixel (M 2 APix) analog silicon retina, in this article, we present comparative tests of two optical flow calculation algorithms operating under lighting conditions from 6 × 10 7 to 1 . 6 × 10 2 W·cm 2 (i.e., from 0.2 to 12,000 lux for human vision). Contrast “time of travel” between two adjacent light-sensitive pixels was determined by thresholding and by cross-correlating the two pixels’ signals, with measurement frequency up to 5 kHz for the 10 local motion sensors of the M 2 APix sensor. While both algorithms adequately measured optical flow between 25 /s and 1000 /s, thresholding gave rise to a lower precision, especially due to a larger number of outliers at higher speeds. Compared to thresholding, cross-correlation also allowed for a higher rate of optical flow output (99 Hz and 1195 Hz, respectively) but required substantially more computational resources. View Full-Text
Keywords: optic flow sensor; sense and avoid; VLSI retina; micro air vehicle (MAV); bionics; bio-inspired robotics; biorobotics optic flow sensor; sense and avoid; VLSI retina; micro air vehicle (MAV); bionics; bio-inspired robotics; biorobotics
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary materials

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Vanhoutte, E.; Mafrica, S.; Ruffier, F.; Bootsma, R.J.; Serres, J. Time-of-Travel Methods for Measuring Optical Flow on Board a Micro Flying Robot. Sensors 2017, 17, 571.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top