ORB-SLAM: A Versatile and Accurate Monocular SLAM System

Mur-Artal, R. (Universidad de Zaragoza) ; Montiel, J.M.M. (Universidad de Zaragoza) ; Tardos, J.D. (Universidad de Zaragoza)
ORB-SLAM: A Versatile and Accurate Monocular SLAM System
Resumen: This paper presents ORB-SLAM, a feature-based monocular simultaneous localization and mapping (SLAM) system that operates in real time, in small and large indoor and outdoor environments. The system is robust to severe motion clutter, allows wide baseline loop closing and relocalization, and includes full automatic initialization. Building on excellent algorithms of recent years, we designed from scratch a novel system that uses the same features for all SLAM tasks: tracking, mapping, relocalization, and loop closing. A survival of the fittest strategy that selects the points and keyframes of the reconstruction leads to excellent robustness and generates a compact and trackable map that only grows if the scene content changes, allowing lifelong operation. We present an exhaustive evaluation in 27 sequences from the most popular datasets. ORB-SLAM achieves unprecedented performance with respect to other state-of-the-art monocular SLAM approaches. For the benefit of the community, we make the source code public.
Idioma: Inglés
DOI: 10.1109/TRO.2015.2463671
Año: 2015
Publicado en: IEEE Transactions on Robotics 31, 5 (2015), 1147-1163
ISSN: 1552-3098

Factor impacto JCR: 2.028 (2015)
Categ. JCR: ROBOTICS rank: 7 / 25 = 0.28 (2015) - Q2 - T1
Factor impacto SCIMAGO:

Financiación: info:eu-repo/grantAgreement/ES/DGA/B121-13
Financiación: info:eu-repo/grantAgreement/ES/MEC/FPU13-04175
Financiación: info:eu-repo/grantAgreement/ES/MINECO/DPI2012-32168
Tipo y forma: Article (PostPrint)
Área (Departamento): Ingeniería de Sistemas y Automática (Departamento de Informática e Ingeniería de Sistemas)

Rights Reserved All rights reserved by journal editor

Exportado de SIDERAL (2017-05-18-11:32:33)

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Articles > Artículos por área > Ingeniería de Sistemas y Automática

 Record created 2015-12-22, last modified 2017-05-18

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