{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,26]],"date-time":"2026-07-26T01:12:09Z","timestamp":1785028329702,"version":"3.55.0"},"reference-count":42,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2017,3,11]],"date-time":"2017-03-11T00:00:00Z","timestamp":1489190400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2013Menten 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 \u00d7  10  \u2212 7       to     1 . 6 \u00d7  10  \u2212 2       W\u00b7cm      \u2212 2      (i.e., from 0.2 to 12,000 lux for human vision). Contrast \u201ctime of travel\u201d between two adjacent light-sensitive pixels was determined by thresholding and by cross-correlating the two pixels\u2019 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      \u2218    \/s and 1000      \u2218    \/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.<\/jats:p>","DOI":"10.3390\/s17030571","type":"journal-article","created":{"date-parts":[[2017,3,13]],"date-time":"2017-03-13T10:26:18Z","timestamp":1489400778000},"page":"571","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Time-of-Travel Methods for Measuring Optical Flow on Board a Micro Flying Robot"],"prefix":"10.3390","volume":"17","author":[{"given":"Erik","family":"Vanhoutte","sequence":"first","affiliation":[{"name":"Aix-Marseille Universit\u00e9, CNRS, ISM UMR7287, 13288 Marseille Cedex 09, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefano","family":"Mafrica","sequence":"additional","affiliation":[{"name":"Aix-Marseille Universit\u00e9, CNRS, ISM UMR7287, 13288 Marseille Cedex 09, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7854-1275","authenticated-orcid":false,"given":"Franck","family":"Ruffier","sequence":"additional","affiliation":[{"name":"Aix-Marseille Universit\u00e9, CNRS, ISM UMR7287, 13288 Marseille Cedex 09, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3719-7586","authenticated-orcid":false,"given":"Reinoud","family":"Bootsma","sequence":"additional","affiliation":[{"name":"Aix-Marseille Universit\u00e9, CNRS, ISM UMR7287, 13288 Marseille Cedex 09, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2840-7932","authenticated-orcid":false,"given":"Julien","family":"Serres","sequence":"additional","affiliation":[{"name":"Aix-Marseille Universit\u00e9, CNRS, ISM UMR7287, 13288 Marseille Cedex 09, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,3,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1152\/physrev.00005.2010","article-title":"Honeybees as a model for the study of visually guided flight, navigation, and biologically inspired robotics","volume":"91","author":"Srinivasan","year":"2011","journal-title":"Physiol. 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