. .. Résultats-qualitatifs,

.. .. Conclusion,

, Algorithme 2 Processus de localisation Entrées : Image, Prior, Map // Initialisation des sorties

, Localization kf = null 2: CorrectAssociations = null 3: C f i nal = Prior // Tentative de localisation initialisée par le prior odométrique. 4: Odometry kf = findClosestKeyframe (Prior, Map) 5: References o = getReferences (Odometry kf , Map) 6: Associations o = track3DPoints, Image, References o , Map), vol.7

, Localization kf = Odometry kf 13: C f i nal = C * o 14: end if // Tentative de localisation initialisée par l'apparence visuelle, Visual kf = fabmap (Image, ApparenceMap) 16: References v = getReferences (Visual kf , Map) 17: Associations v = track3DPoints, vol.8

. Kümmerle, Sorties : Localization kf , CorrectAssociations, C f i nal Ces deux optimisations sont réalisées grâce à la librairie g 2 o, 2011.

, Évaluation expérimentale Dans cette partie, nous présentons une évaluation de notre solution de SLAM métrique visuel

, Notre algorithme a été évalué à partir de données acquises par plusieurs robots Pepper. Ces robots sont équipés d'un dispositif stéréo

, Les images monoculaires utilisées sont de dimensions 640×360. L'image stéréo résultante est la concaténation de ces deux images monoculaires

, Aucune contrainte spécifique n'est appliquée pendant ces expériences. Ainsi, on retrouve dans les données collectées des problèmes typiques tels que des occultations, des objets déplacés ou en déplacement, ainsi que des variations d'intensité lumineuse. Plusieurs acquisitions sont réalisées dans cet environnement, totalisant 950 m parcourus. Ces acquisitions couvrent de nombreux scénarios comme : différentes vitesses du robot, les variations des conditions lumineuses selon l'heure d'acquisition

, Dans toutes nos expériences, notre solution a réussi à fermer la boucle et optimiser la carte de façon cohérente

, Résultats qualitatifs Nous n'avons pas été capables de comparer quantitativement notre système aux méthodes classiques de SLAM visuel (ou visuel-inertiel) à cause des spécificités de nos acquisitions. En effet, aucun algorithme prêt-à-l'emploi trouvé dans la littérature n'a pu délivrer des performances suffisamment bonnes sur les données acquises par Pepper 14

. Geiger, les applications de SLAM visuel actuelles se permettent de monter jusqu'à 30 Hz en embarqué. La base de données du challenge KITTI, souvent utilisée dans ce domaine, propose des données synchronisées à 10 Hz, soit cinq fois plus rapidement que dans notre contexte. breux magasins, 2013.

, Nous avons estimé empiriquement qu'acquérir et traiter les images stéréo toutes les 0,5 s constitue un bon compromis entre la qualité de la carte estimée et l'empreinte computationnelle de l'algorithme. Sur Pepper, une telle fréquence d'acquisition permet un traitement de localisation et cartographie en temps-réel

, Cependant, toutes les fonctions présentées dans l'Algorithme 2 n'ont pas le même impact et n'évoluent pas de la même façon au cours de l'expérience. Nous classons ici ces étapes, de la plus consommatrice à la plus légère en temps de calcul

, FAB-MAP, présenté en partie 3.2, se divise en deux blocs : le premier qui génère le vecteur d'apparence, et le second qui associe à chaque image-clé un score de vraisemblance. La durée de création du vecteur d'apparence est proportionnelle au nombre de points d'intérêt détectés dans l'image requête 16 et dure en moyenne 31,97 ms dans cet environnement. Le calcul des vraisemblances augmente, lui, de manière logarithmique avec le nombre de lieux présents dans la carte, 2011.

, En effet, il dépend à la fois du nombre de références utilisées, du nombre points d'intérêt extraits sur l'image courante, de s'ils ressemblent fortement à des points d'intérêt d'images-clés estimées proches, Le suivi des points dans l'image dépend de nombreux facteurs si bien qu'il est compliqué de prédire son comportement

. Kümmerle, Le raffinement de la pose correspond à quatre optimisations successives, 2011.

, Notons que pour éviter une redondance de description, seule l'image gauche est utilisée pour construire ce vecteur d'apparence

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