DPPTAM: Dense Piecewise Planar Tracking and Mapping  from a Monocular Sequence
Resumen: This paper proposes a direct monocular SLAM algorithm that estimates a dense reconstruction of a scene in real-time on a CPU. Highly textured image areas are mapped using standard direct mapping techniques [1], that minimize the photometric error across different views. We make the assumption that homogeneous-color regions belong to approximately planar areas. Our contribution is a new algorithm for the estimation of such planar areas, based on the information of a superpixel segmentation and the semidense map from highly textured areas.
We compare our approach against several alternatives using the public TUM dataset [2] and additional live experiments with a hand-held camera. We demonstrate that our proposal for piecewise planar monocular SLAM is faster, more accurate and more robust than the piecewise planar baseline [3]. In addition, our experimental results show how the depth regularization of monocular maps can damage its accuracy, being the piecewise planar assumption a reasonable option in indoor scenarios.

Idioma: Inglés
DOI: 10.1109/IROS.2015.7354184
Año: 2015
Publicado en: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems 2015 (2015), [8 pp.]
ISSN: 2153-0858

Factor impacto SCIMAGO: 0.843 - Computer Science Applications - Software - Control and Systems Engineering - Computer Vision and Pattern Recognition

Financiación: info:eu-repo/grantAgreement/ES/MINECO/DPI2012-32168
Financiación: info:eu-repo/grantAgreement/ES/MINECO/IPT2012-1309-430000
Tipo y forma: Article (PrePrint)
Área (Departamento): Área Ingen.Sistemas y Automát. (Dpto. Informát.Ingenie.Sistms.)

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