IEEE Transactions on Robotics· 2015Q1
ORB-SLAM: A Versatile and Accurate Monocular SLAM System
- 6,506citations
- Q1SCImago
- 2015year
Short summary
ORB-SLAM is a novel, feature-based monocular SLAM system that operates in real-time across diverse environments and is robust to motion clutter.
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Key points
- Uses a unified feature set for all SLAM tasks (tracking, mapping, relocalization, loop closing).
- Achieves real-time operation in small/large indoor/outdoor environments.
- Robust to severe motion clutter and allows wide baseline loop closing.
- Includes automatic initialization and a 'survival of the fittest' strategy for map maintenance.
- Outperforms other state-of-the-art monocular SLAM systems in an exhaustive evaluation.
AI-generated from the title and abstract; the full text is not read.
Abstract
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.
The authors' abstract, as published at the source. IEEE Transactions on Robotics, 2015 · DOI ↗
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Field: Aerospace Engineering
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