ORB-SLAM: a Versatile and Accurate Monocular SLAM System
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Abstract (arXiv)
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.
Key facts. Cited by 983 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Feb 2015.
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Citing papers per year: 2018: 1; 2019: 0; 2020: 0; 2021: 143; 2022: 161; 2023: 165; 2024: 181; 2025: 190; 2026: 142.
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12 most cited of 949 All 949 (JSON)
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- 2026UltraMatch: Transport Path Routing for Ultra-Fast and Memory-Efficient Image Matching29 Sep 2026
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- 2026Visuomotor Robotic Pruning in Planar Orchards Using Hybrid Reinforcement Learning21 Sep 2026
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- 2026Cube-Splat: High-Fidelity 360deg Gaussian Splatting SLAM via Cubemap Factorization and Adjoint-Consistent Optimization18 Sep 2026
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- 2026GenStream: Semantic Streaming Framework for Generative Reconstruction of Human-centric Media16 Sep 2026
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