Mostrar el registro sencillo del ítem
dc.contributor.author | Jaenal, Alberto | |
dc.contributor.author | Moreno, Francisco-Angel | |
dc.contributor.author | Gonzalez-Jimenez, Javier | |
dc.date.accessioned | 2024-02-19T15:26:46Z | |
dc.date.available | 2024-02-19T15:26:46Z | |
dc.date.issued | 2021-04-02 | |
dc.identifier.other | http://hdl.handle.net/10668/3818 | |
dc.identifier.uri | http://hdl.handle.net/20.500.12105/18304 | |
dc.description.abstract | This paper addresses appearance-based robot localization in 2D with a sparse, lightweight map of the environment composed of descriptor-pose image pairs. Based on previous research in the field, we assume that image descriptors are samples of a low-dimensional Descriptor Manifold that is locally articulated by the camera pose. We propose a piecewise approximation of the geometry of such Descriptor Manifold through a tessellation of so-called Patches of Smooth Appearance Change (PSACs), which defines our appearance map. Upon this map, the presented robot localization method applies both a Gaussian Process Particle Filter (GPPF) to perform camera tracking and a Place Recognition (PR) technique for relocalization within the most likely PSACs according to the observed descriptor. A specific Gaussian Process (GP) is trained for each PSAC to regress a Gaussian distribution over the descriptor for any particle pose lying within that PSAC. The evaluation of the observed descriptor in this distribution gives us a likelihood, which is used as the weight for the particle. Besides, we model the impact of appearance variations on image descriptors as a white noise distribution within the GP formulation, ensuring adequate operation under lighting and scene appearance changes with respect to the conditions in which the map was constructed. A series of experiments with both real and synthetic images show that our method outperforms state-of-the-art appearance-based localization methods in terms of robustness and accuracy, with median errors below 0.3 m and 6°. | |
dc.description.sponsorship | This research was funded by: Government of Spain grant number FPU17/04512; by the “I Plan Propio de Investigación, Transferencia y Divulgación Científica” of the University of Málaga; and under projects ARPEGGIO (PID2020-117057) and WISER (DPI2017-84827-R) financed by the Government of Spain and European Regional Development’s funds (FEDER). | |
dc.language.iso | eng | |
dc.publisher | Multidisciplinary Digital Publishing Institute (MDPI) | |
dc.type.hasVersion | VoR | |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Appearance-based localization | |
dc.subject | Computer vision | |
dc.subject | Gaussian processes | |
dc.subject | Manifold learning | |
dc.subject | Robot vision systems | |
dc.subject | Indoor positioning | |
dc.subject | Image manifold | |
dc.subject | Descriptor manifold | |
dc.subject | Aprendizaje | |
dc.subject | Descriptores | |
dc.subject | Reconocimiento de normas patrones automatizadas | |
dc.subject | Ambiente | |
dc.subject | Métodos | |
dc.subject | Inteligencia artificial | |
dc.subject.mesh | Lighting | |
dc.subject.mesh | Pattern Recognition, Automated | |
dc.subject.mesh | Imaging, Three-Dimensional | |
dc.subject.mesh | Image Interpretation, Computer-Assisted | |
dc.subject.mesh | Uncertainty | |
dc.subject.mesh | Environment | |
dc.subject.mesh | Normal Distribution | |
dc.subject.mesh | Artificial Intelligence | |
dc.title | Appearance-Based Sequential Robot Localization Using a Patchwise Approximation of a Descriptor Manifold | |
dc.type | research article | |
dc.rights.license | Attribution 4.0 International | * |
dc.identifier.pubmedID | 33918493 | es_ES |
dc.identifier.doi | 10.3390/s21072483 | |
dc.identifier.e-issn | 1424-8220 | es_ES |
dc.relation.publisherversion | https://www.mdpi.com/1424-8220/21/7/2483/htm | es |
dc.identifier.journal | Sensors | es_ES |
dc.rights.accessRights | open access | es_ES |
dc.contributor.authoraffiliation | [Jaenal,A; Moreno,FA; Gonzalez-Jimenez,J] Machine Perception and Intelligent Robotics Group (MAPIR), Department of System Engineering and Automation Biomedical Research Institute of Malaga (IBIMA), University of Malaga, Málaga, Spain. |
Ficheros en el ítem
Ficheros | Tamaño | Formato | Ver |
---|---|---|---|
No hay ficheros asociados a este ítem. |