dor_id: 4129203

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510.0.#.a: Scopus, Directory of Open Access Journals (DOAJ); Sistema Regional de Información en Línea para Revistas Científicas de América Latina, el Caribe, España y Portugal (Latindex); Indice de Revistas Latinoamericanas en Ciencias (Periódica); La Red de Revistas Científicas de América Latina y el Caribe, España y Portugal (Redalyc); Consejo Nacional de Ciencia y Tecnología (CONACyT); Google Scholar Citation

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336.#.#.3: Artículo de Investigación

336.#.#.a: Artículo

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351.#.#.b: Journal of Applied Research and Technology

351.#.#.a: Artículos

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270.1.#.p: Revistas UNAM. Dirección General de Publicaciones y Fomento Editorial, UNAM en revistas@unam.mx

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270.#.#.d: MX

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883.#.#.u: https://revistas.unam.mx/catalogo/

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856.4.0.u: https://jart.icat.unam.mx/index.php/jart/article/view/991/878

100.1.#.a: Trabes, Emanuel; Avila, Luis; Dondo Gazzano, Julio; Sosa Páez, Carlos

524.#.#.a: Trabes, Emanuel, et al. (2021). Dense monocular Simultaneous Localization and Mapping by direct surfel optimization. Journal of Applied Research and Technology; Vol. 19 Núm. 6, 2021; 644-652. Recuperado de https://repositorio.unam.mx/contenidos/4129203

245.1.0.a: Dense monocular Simultaneous Localization and Mapping by direct surfel optimization

502.#.#.c: Universidad Nacional Autónoma de México

561.1.#.a: Instituto de Ciencias Aplicadas y Tecnología, UNAM

264.#.0.c: 2021

264.#.1.c: 2021-12-31

653.#.#.a: Depth Estimation; Visual Odometry; SLAM

506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY-NC-SA 4.0 Internacional, https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico gabriel.ascanio@icat.unam.mx

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520.3.#.a: This work presents a novel approach for monocular dense Simultaneous Localization and Mapping. The surface to be estimated is represented as a piecewise planar surface, defined as a group of surfels each having as parameters its position and normal. These parameters are then directly estimated from the raw camera pixels measurements, by a Gauss-Newton iterative process. The representation of the surface as a group of surfels has several advantages. It allows the recovery of robust and accurate pixel depths, without the need to use a computationally demanding depth regularization schema. This has the further advantage of avoiding the use of a physically unlikely surface smoothness prior. New surfels can be correctly initialized from the information present in nearby surfels, avoiding also the need to use an expensive initialization routine commonly needed in Gauss-Newton methods. The method was written in the GLSL shading language, allowing the usage of GPU thus achieving real-time. The method was tested against several datasets, showing both its depth and normal estimation correctness, and its scene reconstruction quality. The results presented here showcase the usefulness of the more physically grounded piecewise planar scene depth prior, instead of the more commonly pixel depth independence and smoothness prior.

773.1.#.t: Journal of Applied Research and Technology; Vol. 19 Núm. 6 (2021); 644-652

773.1.#.o: https://jart.icat.unam.mx/index.php/jart

022.#.#.a: ISSN electrónico: 2448-6736; ISSN: 1665-6423

310.#.#.a: Bimestral

300.#.#.a: Páginas: 644-652

264.#.1.b: Instituto de Ciencias Aplicadas y Tecnología, UNAM

doi: https://doi.org/10.22201/icat.24486736e.2021.19.6.991

harvesting_date: 2023-11-08 13:10:00.0

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Artículo

Dense monocular Simultaneous Localization and Mapping by direct surfel optimization

Trabes, Emanuel; Avila, Luis; Dondo Gazzano, Julio; Sosa Páez, Carlos

Instituto de Ciencias Aplicadas y Tecnología, UNAM, publicado en Journal of Applied Research and Technology, y cosechado de Revistas UNAM

Licencia de uso

Procedencia del contenido

Cita

Trabes, Emanuel, et al. (2021). Dense monocular Simultaneous Localization and Mapping by direct surfel optimization. Journal of Applied Research and Technology; Vol. 19 Núm. 6, 2021; 644-652. Recuperado de https://repositorio.unam.mx/contenidos/4129203

Descripción del recurso

Autor(es)
Trabes, Emanuel; Avila, Luis; Dondo Gazzano, Julio; Sosa Páez, Carlos
Tipo
Artículo de Investigación
Área del conocimiento
Ingenierías
Título
Dense monocular Simultaneous Localization and Mapping by direct surfel optimization
Fecha
2021-12-31
Resumen
This work presents a novel approach for monocular dense Simultaneous Localization and Mapping. The surface to be estimated is represented as a piecewise planar surface, defined as a group of surfels each having as parameters its position and normal. These parameters are then directly estimated from the raw camera pixels measurements, by a Gauss-Newton iterative process. The representation of the surface as a group of surfels has several advantages. It allows the recovery of robust and accurate pixel depths, without the need to use a computationally demanding depth regularization schema. This has the further advantage of avoiding the use of a physically unlikely surface smoothness prior. New surfels can be correctly initialized from the information present in nearby surfels, avoiding also the need to use an expensive initialization routine commonly needed in Gauss-Newton methods. The method was written in the GLSL shading language, allowing the usage of GPU thus achieving real-time. The method was tested against several datasets, showing both its depth and normal estimation correctness, and its scene reconstruction quality. The results presented here showcase the usefulness of the more physically grounded piecewise planar scene depth prior, instead of the more commonly pixel depth independence and smoothness prior.
Tema
Depth Estimation; Visual Odometry; SLAM
Idioma
eng
ISSN
ISSN electrónico: 2448-6736; ISSN: 1665-6423

Enlaces