2D/2D approaches for SFM using an asynchronous multi-camera network
Identifieur interne : 000007 ( Hal/Corpus ); précédent : 000006; suivant : 0000082D/2D approaches for SFM using an asynchronous multi-camera network
Auteurs : Rawia MhiriSource :
Descripteurs français
English descriptors
- mix :
Abstract
Driver assistance systems and autonomous vehicles have reached a certain maturity in recent years through the use of advanced technologies. A fundamental step for these systems is the motion and the structure estimation (Structure From Motion) that accomplish several tasks, including the detection of obstacles and road marking, localisation and mapping. To estimate their movements, such systems use relatively expensive sensors. In order to market such systems on a large scale, it is necessary to develop applications with low cost devices. In this context, vision systems is a good alternative. A new method based on 2D/2D approaches from an asynchronous multi-camera network is presented to obtain the motion and the 3D structure at the absolute scale, focusing on estimating the scale factors. The proposed method, called Triangle Method, is based on the use of three images forming a. triangle shape: two images from the same camera and an image from a neighboring camera. The algorithrn has three assumptions: the cameras share common fields of view (two by two), the path between two consecutive images from a single camera is approximated by a line segment, and the cameras are calibrated. The extrinsic calibration between two cameras combined with the assumption of rectilinear motion of the system allows to estimate the absolute scale factors. The proposed method is accurate and robust for straight trajectories and present satisfactory results for curve trajectories. To refine the initial estimation, some en-ors due to the inaccuracies of the scale estimation are improved by an optimization method: a local bundle adjustment applied only on the absolute scale factors and the 3D points. The presented approach is validated on sequences of real road scenes, and evaluated with respect to the ground truth obtained through a differential GPS. Finally, another fundamental application in the fields of driver assistance and automated driving is road and obstacles detection. A method is presented for an asynchronous system based on sparse disparity maps
Url:
Links to Exploration step
Hal:tel-01278894Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">2D/2D approaches for SFM using an asynchronous multi-camera network</title><title xml:lang="fr">Approches 2D/2D pour le SFM à partir d'un réseau de caméras asynchrones</title><author><name sortKey="Mhiri, Rawia" sort="Mhiri, Rawia" uniqKey="Mhiri R" first="Rawia" last="Mhiri">Rawia Mhiri</name><affiliation><hal:affiliation type="laboratory" xml:id="struct-446467" status="INCOMING"><orgName>Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes</orgName><orgName type="acronym">LITIS</orgName><desc><address><addrLine>Avenue de l'Université UFR des Sciences et Techniques 76800 Saint-Etienne du Rouvray</addrLine><country key="FR"></country></address><ref type="url">http://www.litislab.eu</ref></desc><listRelation><relation active="#struct-420992" type="direct"></relation><relation active="#struct-446463" type="direct"></relation><relation active="#struct-446464" type="direct"></relation><relation active="#struct-446465" type="direct"></relation></listRelation><tutelles><tutelle active="#struct-420992" type="direct"><org type="institution" xml:id="struct-420992" status="INCOMING"><orgName>Université du Havre</orgName></org></tutelle><tutelle active="#struct-446463" type="direct"><org type="institution" xml:id="struct-446463" status="INCOMING"><orgName>Institut National des Sciences Appliquées [INSA] - Rouen</orgName></org></tutelle><tutelle active="#struct-446464" type="direct"><org type="institution" xml:id="struct-446464" status="INCOMING"><orgName>Institut National des Sciences Appliquées (INSA) - Rouen</orgName></org></tutelle><tutelle active="#struct-446465" type="direct"><org type="institution" xml:id="struct-446465" status="INCOMING"><orgName>Université de Rouen : EA4108</orgName></org></tutelle></tutelles></hal:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">HAL</idno><idno type="RBID">Hal:tel-01278894</idno><idno type="halId">tel-01278894</idno><idno type="halUri">https://tel.archives-ouvertes.fr/tel-01278894</idno><idno type="url">https://tel.archives-ouvertes.fr/tel-01278894</idno><date when="2015-12-14">2015-12-14</date><idno type="wicri:Area/Hal/Corpus">000007</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">2D/2D approaches for SFM using an asynchronous multi-camera network</title><title xml:lang="fr">Approches 2D/2D pour le SFM à partir d'un réseau de caméras asynchrones</title><author><name sortKey="Mhiri, Rawia" sort="Mhiri, Rawia" uniqKey="Mhiri R" first="Rawia" last="Mhiri">Rawia Mhiri</name><affiliation><hal:affiliation type="laboratory" xml:id="struct-446467" status="INCOMING"><orgName>Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes</orgName><orgName type="acronym">LITIS</orgName><desc><address><addrLine>Avenue de l'Université UFR des Sciences et Techniques 76800 Saint-Etienne du Rouvray</addrLine><country key="FR"></country></address><ref type="url">http://www.litislab.eu</ref></desc><listRelation><relation active="#struct-420992" type="direct"></relation><relation active="#struct-446463" type="direct"></relation><relation active="#struct-446464" type="direct"></relation><relation active="#struct-446465" type="direct"></relation></listRelation><tutelles><tutelle active="#struct-420992" type="direct"><org type="institution" xml:id="struct-420992" status="INCOMING"><orgName>Université du Havre</orgName></org></tutelle><tutelle active="#struct-446463" type="direct"><org type="institution" xml:id="struct-446463" status="INCOMING"><orgName>Institut National des Sciences Appliquées [INSA] - Rouen</orgName></org></tutelle><tutelle active="#struct-446464" type="direct"><org type="institution" xml:id="struct-446464" status="INCOMING"><orgName>Institut National des Sciences Appliquées (INSA) - Rouen</orgName></org></tutelle><tutelle active="#struct-446465" type="direct"><org type="institution" xml:id="struct-446465" status="INCOMING"><orgName>Université de Rouen : EA4108</orgName></org></tutelle></tutelles></hal:affiliation></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>Asynchronous multi-camera system</term><term>Local bundle adjustment</term><term>Obstacle detection</term><term>Structure from motion</term><term>Triangle-based method</term><term>Visual odometry</term></keywords><keywords scheme="mix" xml:lang="fr"><term>Ajustement de faisceaux local</term><term>Méthode des triangles</term><term>Odométrie visuelle</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Driver assistance systems and autonomous vehicles have reached a certain maturity in recent years through the use of advanced technologies. A fundamental step for these systems is the motion and the structure estimation (Structure From Motion) that accomplish several tasks, including the detection of obstacles and road marking, localisation and mapping. To estimate their movements, such systems use relatively expensive sensors. In order to market such systems on a large scale, it is necessary to develop applications with low cost devices. In this context, vision systems is a good alternative. A new method based on 2D/2D approaches from an asynchronous multi-camera network is presented to obtain the motion and the 3D structure at the absolute scale, focusing on estimating the scale factors. The proposed method, called Triangle Method, is based on the use of three images forming a. triangle shape: two images from the same camera and an image from a neighboring camera. The algorithrn has three assumptions: the cameras share common fields of view (two by two), the path between two consecutive images from a single camera is approximated by a line segment, and the cameras are calibrated. The extrinsic calibration between two cameras combined with the assumption of rectilinear motion of the system allows to estimate the absolute scale factors. The proposed method is accurate and robust for straight trajectories and present satisfactory results for curve trajectories. To refine the initial estimation, some en-ors due to the inaccuracies of the scale estimation are improved by an optimization method: a local bundle adjustment applied only on the absolute scale factors and the 3D points. The presented approach is validated on sequences of real road scenes, and evaluated with respect to the ground truth obtained through a differential GPS. Finally, another fundamental application in the fields of driver assistance and automated driving is road and obstacles detection. A method is presented for an asynchronous system based on sparse disparity maps</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">2D/2D approaches for SFM using an asynchronous multi-camera network</title><title xml:lang="fr">Approches 2D/2D pour le SFM à partir d'un réseau de caméras asynchrones</title><author role="aut"><persName><forename type="first">Rawia</forename><surname>Mhiri</surname></persName><email></email><idno type="halauthor">1020421</idno><idno type="IdRef">http://www.idref.fr/191309214</idno><affiliation ref="#struct-446467"></affiliation></author><editor role="depositor"><persName><forename>ABES</forename><surname>STAR</surname></persName><email>thelec@abes.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2016-02-25 10:02:32</date><date type="whenModified">2016-03-02 03:03:49</date><date type="whenReleased">2016-02-25 15:23:48</date><date type="whenProduced">2015-12-14</date><date type="whenEndEmbargoed">2016-02-25</date><ref type="file" target="https://tel.archives-ouvertes.fr/tel-01278894/document"><date notBefore="2016-02-25"></date></ref><ref type="file" subtype="author" n="1" target="https://tel.archives-ouvertes.fr/tel-01278894/file/Mhiri.pdf"><date notBefore="2016-02-25"></date></ref></edition><respStmt><resp>contributor</resp><name key="131274"><persName><forename>ABES</forename><surname>STAR</surname></persName><email>thelec@abes.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">tel-01278894</idno><idno type="halUri">https://tel.archives-ouvertes.fr/tel-01278894</idno><idno type="halBibtex">mhiri:tel-01278894</idno><idno type="halRefHtml">Vision par ordinateur et reconnaissance de formes [cs.CV]. INSA de Rouen, 2015. Français. <NNT : 2015ISAM0014></idno><idno type="halRef">Vision par ordinateur et reconnaissance de formes [cs.CV]. INSA de Rouen, 2015. Français. <NNT : 2015ISAM0014></idno></publicationStmt><seriesStmt><idno type="stamp" n="STAR">STAR - Dépôt national des thèses électroniques</idno><idno type="stamp" n="UNIV-LEHAVRE">Université du Havre</idno><idno type="stamp" n="UNIV-ROUEN">Université de Rouen</idno><idno type="stamp" n="LITIS">Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes</idno></seriesStmt><notesStmt></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">2D/2D approaches for SFM using an asynchronous multi-camera network</title><title xml:lang="fr">Approches 2D/2D pour le SFM à partir d'un réseau de caméras asynchrones</title><author role="aut"><persName><forename type="first">Rawia</forename><surname>Mhiri</surname></persName><idno type="halAuthorId">1020421</idno><idno type="IdRef">http://www.idref.fr/191309214</idno><affiliation ref="#struct-446467"></affiliation></author></analytic><monogr><idno type="nnt">2015ISAM0014</idno><imprint><date type="dateDefended">2015-12-14</date></imprint><authority type="institution">INSA de Rouen</authority><authority type="school">Ecole doctorale Sciences Physiques Mathématiques et de l'Information pour l'ingénieur (Saint-Etienne-du-Rouvray, Seine-Maritime)</authority><authority type="supervisor">Abdelaziz Bensrhair</authority><authority type="supervisor">Stéphane Mousset</authority><authority type="jury">Didier Aubert [Président]</authority><authority type="jury">Samia Bouchafa [Rapporteur]</authority><authority type="jury">David Fofi [Rapporteur]</authority><authority type="jury">Pascal Vasseur</authority></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="fr">French</language></langUsage><textClass><keywords scheme="author"><term xml:lang="en">Obstacle detection</term><term xml:lang="en">Structure from motion</term><term xml:lang="en">Visual odometry</term><term xml:lang="en">Asynchronous multi-camera system</term><term xml:lang="en">Triangle-based method</term><term xml:lang="en">Local bundle adjustment</term><term xml:lang="fr">Odométrie visuelle</term><term xml:lang="fr">Méthode des triangles</term><term xml:lang="fr">Ajustement de faisceaux local</term></keywords><classCode scheme="halDomain" n="info.info-cv">Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]</classCode><classCode scheme="halTypology" n="THESE">Theses</classCode></textClass><abstract xml:lang="en">Driver assistance systems and autonomous vehicles have reached a certain maturity in recent years through the use of advanced technologies. A fundamental step for these systems is the motion and the structure estimation (Structure From Motion) that accomplish several tasks, including the detection of obstacles and road marking, localisation and mapping. To estimate their movements, such systems use relatively expensive sensors. In order to market such systems on a large scale, it is necessary to develop applications with low cost devices. In this context, vision systems is a good alternative. A new method based on 2D/2D approaches from an asynchronous multi-camera network is presented to obtain the motion and the 3D structure at the absolute scale, focusing on estimating the scale factors. The proposed method, called Triangle Method, is based on the use of three images forming a. triangle shape: two images from the same camera and an image from a neighboring camera. The algorithrn has three assumptions: the cameras share common fields of view (two by two), the path between two consecutive images from a single camera is approximated by a line segment, and the cameras are calibrated. The extrinsic calibration between two cameras combined with the assumption of rectilinear motion of the system allows to estimate the absolute scale factors. The proposed method is accurate and robust for straight trajectories and present satisfactory results for curve trajectories. To refine the initial estimation, some en-ors due to the inaccuracies of the scale estimation are improved by an optimization method: a local bundle adjustment applied only on the absolute scale factors and the 3D points. The presented approach is validated on sequences of real road scenes, and evaluated with respect to the ground truth obtained through a differential GPS. Finally, another fundamental application in the fields of driver assistance and automated driving is road and obstacles detection. A method is presented for an asynchronous system based on sparse disparity maps</abstract><abstract xml:lang="fr">Les systèmes d'aide à la conduite et les travaux concernant le véhicule autonome ont atteint une certaine maturité durant ces dernières aimées grâce à l'utilisation de technologies avancées. Une étape fondamentale pour ces systèmes porte sur l'estimation du mouvement et de la structure de l'environnement (Structure From Motion) pour accomplir plusieurs tâches, notamment la détection d'obstacles et de marquage routier, la localisation et la cartographie. Pour estimer leurs mouvements, de tels systèmes utilisent des capteurs relativement chers. Pour être commercialisés à grande échelle, il est alors nécessaire de développer des applications avec des dispositifs bas coûts. Dans cette optique, les systèmes de vision se révèlent une bonne alternative. Une nouvelle méthode basée sur des approches 2D/2D à partir d'un réseau de caméras asynchrones est présentée afin d'obtenir le déplacement et la structure 3D à l'échelle absolue en prenant soin d'estimer les facteurs d'échelle. La méthode proposée, appelée méthode des triangles, se base sur l'utilisation de trois images formant un triangle : deux images provenant de la même caméra et une image provenant d'une caméra voisine. L'algorithme admet trois hypothèses: les caméras partagent des champs de vue communs (deux à deux), la trajectoire entre deux images consécutives provenant d'une même caméra est approximée par un segment linéaire et les caméras sont calibrées. La connaissance de la calibration extrinsèque entre deux caméras combinée avec l'hypothèse de mouvement rectiligne du système, permet d'estimer les facteurs d'échelle absolue. La méthode proposée est précise et robuste pour les trajectoires rectilignes et présente des résultats satisfaisants pour les virages. Pour affiner l'estimation initiale, certaines erreurs dues aux imprécisions dans l'estimation des facteurs d'échelle sont améliorées par une méthode d'optimisation : un ajustement de faisceaux local appliqué uniquement sur les facteurs d'échelle absolue et sur les points 3D. L'approche présentée est validée sur des séquences de scènes routières réelles et évaluée par rapport à la vérité terrain obtenue par un GPS différentiel. Une application fondamentale dans les domaines d'aide à la conduite et de la conduite automatisée est la détection de la route et d'obstacles. Pour un système asynchrone, une première approche pour traiter cette application est présentée en se basant sur des cartes de disparité éparses.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Wicri/France/explor/LeHavreV1/Data/Hal/Corpus
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000007 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Hal/Corpus/biblio.hfd -nk 000007 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Wicri/France
|area= LeHavreV1
|flux= Hal
|étape= Corpus
|type= RBID
|clé= Hal:tel-01278894
|texte= 2D/2D approaches for SFM using an asynchronous multi-camera network
}}
| This area was generated with Dilib version V0.6.25. |  |