Cargando…

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

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-adapti...

Descripción completa

Detalles Bibliográficos
Autores principales: Vanhoutte, Erik, Mafrica, Stefano, Ruffier, Franck, Bootsma, Reinoud J., Serres, Julien
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5375857/
https://www.ncbi.nlm.nih.gov/pubmed/28287484
http://dx.doi.org/10.3390/s17030571
Descripción
Sumario: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 [Formula: see text] APix) analog silicon retina, in this article, we present comparative tests of two optical flow calculation algorithms operating under lighting conditions from [Formula: see text] to [Formula: see text] W·cm [Formula: see text] (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 [Formula: see text] APix sensor. While both algorithms adequately measured optical flow between 25 [Formula: see text] /s and 1000 [Formula: see text] /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.