Pose self-calibration of stereo vision systems for autonomous vehicle applications

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dc.contributor.author Musleh Lancis, Basam
dc.contributor.author Martín Gómez, David
dc.contributor.author Armingol Moreno, José María
dc.contributor.author Escalera Hueso, Arturo de la
dc.date.accessioned 2019-01-15T11:58:46Z
dc.date.available 2019-01-15T11:58:46Z
dc.date.issued 2016-09-14
dc.identifier.bibliographicCitation Musleh, B., Martín, D., Armingol, J.M., De la Escalera, A. (2016). Pose Self-Calibration of Stereo Vision Systems for Autonomous Vehicle Applications. Sensors, 16 (9), 1492.
dc.identifier.issn 1424-8220
dc.identifier.uri http://hdl.handle.net/10016/27903
dc.description.abstract Nowadays, intelligent systems applied to vehicles have grown very rapidly; their goal is not only the improvement of safety, but also making autonomous driving possible. Many of these intelligent systems are based on making use of computer vision in order to know the environment and act accordingly. It is of great importance to be able to estimate the pose of the vision system because the measurement matching between the perception system (pixels) and the vehicle environment (meters) depends on the relative position between the perception system and the environment. A new method of camera pose estimation for stereo systems is presented in this paper, whose main contribution regarding the state of the art on the subject is the estimation of the pitch angle without being affected by the roll angle. The validation of the self-calibration method is accomplished by comparing it with relevant methods of camera pose estimation, where a synthetic sequence is used in order to measure the continuous error with a ground truth. This validation is enriched by the experimental results of the method in real traffic environments.
dc.description.sponsorship This work was supported by the Spanish Government through the CICYTprojects (TRA2013-48314-C3-1-R and TRA2015-63708-R) and Comunidad de Madrid through SEGVAUTO-TRIES (S2013/MIT-2713).
dc.format.extent 23
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher MDPI
dc.rights © 2016 by the authors; licensee MDPI, Basel, Switzerland.
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Camera pose estimation
dc.subject.other Stereo vision
dc.subject.other Advanced driver assistance system
dc.subject.other Intelligent vehicles
dc.subject.other Extrinsic camera calibration
dc.subject.other Efficient
dc.subject.other Image
dc.title Pose self-calibration of stereo vision systems for autonomous vehicle applications
dc.type article
dc.description.status Publicado
dc.subject.eciencia Robótica e Informática Industrial
dc.identifier.doi https://doi.org/10.3390/s16091492
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. TRA2013-48314-C3-1-R
dc.relation.projectID Gobierno de España. TRA2015-63708-R
dc.relation.projectID Comunidad de Madrid. S2013/MIT-2713
dc.type.version publishedVersion
dc.identifier.publicationissue 9
dc.identifier.publicationtitle Sensors
dc.identifier.publicationvolume 16
dc.identifier.uxxi AR/0000018352
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