Abstract

This paper presents ORB-SLAM, a feature-based monocular 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.

Affiliated Institutions

Related Publications

Publication Info

Year
2015
Type
article
Volume
31
Issue
5
Pages
1147-1163
Citations
6097
Access
Closed

External Links

Download PDF (Free) View on DOI.org arXiv Semantic Scholar

Social Impact

Altmetric
PlumX Metrics

Social media, news, blog, policy document mentions

Citation Metrics

6097
OpenAlex
885
Influential
5972
CrossRef

Cite This

Raul Mur Artal, J.M.M. Montiel, Juan D. Tardós et al. (2015). ORB-SLAM: A Versatile and Accurate Monocular SLAM System. IEEE Transactions on Robotics , 31 (5) , 1147-1163. https://doi.org/10.1109/tro.2015.2463671

Identifiers

DOI
10.1109/tro.2015.2463671
arXiv
1502.00956

Data Quality

Data completeness: 84%