Estimation de mouvement d'un drone à partir d'un capteur stéréo hybride
Identifieur interne : 000638 ( France/Analysis ); précédent : 000637; suivant : 000639Estimation de mouvement d'un drone à partir d'un capteur stéréo hybride
Auteurs : Damien Eynard [France] ; Pascal Vasseur [France] ; Cédric Demonceaux [France] ; Vincent Fremont [France]Source :
Abstract
Motion and velocity are two of the most important parameters to be known for an Unmanned Aerial Vehicle (UAV) especially during critical maneuvers such as landing or steady flight. In this paper, we present mixed stereoscopic vision system made of a fish-eye camera and a perspective camera for motion estimation. Contrary to classical stereoscopic systems based on feature matching between cameras, we propose an algorithm which tracks and exploits points in each camera independently. The omnidirectional view estimates the orientation of the motion while the perspective view contribute to estimate the scale of the translation and brings accuracy. By fusing points tracked in each camera and knowing the rotation between two consecutive positions, we can estimate robustly the translation and the velocity using the two-points algorithm. Then motion is filtered by Kalman filter to remove bad estimations. By experimental results on UAV, we validate the approach.
Url:
Affiliations:
- France
- Haute-Normandie, Région Bourgogne, Région Normandie
- Dijon, Le Havre, Rouen
- Université de Bourgogne, Université de Bourgogne Franche-Comté, Université de Rouen, Université du Havre
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Hal:inria-00598255Le document en format XML
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In this paper, we present mixed stereoscopic vision system made of a fish-eye camera and a perspective camera for motion estimation. Contrary to classical stereoscopic systems based on feature matching between cameras, we propose an algorithm which tracks and exploits points in each camera independently. The omnidirectional view estimates the orientation of the motion while the perspective view contribute to estimate the scale of the translation and brings accuracy. By fusing points tracked in each camera and knowing the rotation between two consecutive positions, we can estimate robustly the translation and the velocity using the two-points algorithm. Then motion is filtered by Kalman filter to remove bad estimations. By experimental results on UAV, we validate the approach.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Haute-Normandie</li><li>Région Bourgogne</li><li>Région Normandie</li></region><settlement><li>Dijon</li><li>Le Havre</li><li>Rouen</li></settlement><orgName><li>Université de Bourgogne</li><li>Université de Bourgogne Franche-Comté</li><li>Université de Rouen</li><li>Université du Havre</li></orgName></list><tree><country name="France"><noRegion><name sortKey="Eynard, Damien" sort="Eynard, Damien" uniqKey="Eynard D" first="Damien" last="Eynard">Damien Eynard</name></noRegion><name sortKey="Demonceaux, Cedric" sort="Demonceaux, Cedric" uniqKey="Demonceaux C" first="Cédric" last="Demonceaux">Cédric Demonceaux</name><name sortKey="Fremont, Vincent" sort="Fremont, Vincent" uniqKey="Fremont V" first="Vincent" last="Fremont">Vincent Fremont</name><name sortKey="Vasseur, Pascal" sort="Vasseur, Pascal" uniqKey="Vasseur P" first="Pascal" last="Vasseur">Pascal Vasseur</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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