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MonoSLAM: Real-Time Single Camera SLAM
Abstract
We present a real-time algorithm which can recover the 3D trajectory of a monocular camera, moving rapidly through a previously unknown scene. Our system, which we dub MonoSLAM, is the first successful application of the SLAM methodology from mobile robotics to the "pure vision" domain of a single uncontrolled camera, achieving real time but drift-free performance inaccessible to Structure from Motion approaches. The core of the approach is the online creation of a sparse but persistent map of natural landmarks within a probabilistic framework. Our key novel contributions include an active approach to mapping and measurement, the use of a general motion model for smooth camera movement, and solutions for monocular feature initialization and feature orientation estimation. Together, these add up to an extremely efficient and robust algorithm which runs at 30 Hz with standard PC and camera hardware. This work extends the range of robotic systems in which SLAM can be usefully applied, but also opens up new areas. We present applications of MonoSLAM to real-time 3D localization and mapping for a high-performance full-size humanoid robot and live augmented reality with a hand-held camera.
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“For example, UVIO [146] employs a transformer backbone to jointly learn from visual and IMU inputs, significantly improving pose estimation in dynamic environments.”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [146]
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Research works printed but not linked: 6
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- Neira, Mobile Robot Localisation and Map Building Using Monocular Vision
- 10.1007/BFb0054781 · DOI
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Metadata
- Authors
- Davison, A. J., Reid, I. D., Molton, N. D., & Stasse, O.
- Affiliations
- University of Oxford (GB)
- Centre National de la Recherche Scientifique (FR)
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2007-06
- Volume
- 29
- Issue
- 6
- Pages
- 1052-1067
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2152671441
- Cited by
- 3,959
- References
- 57
- Citations by year
- 2026: 108 · 2025: 240 · 2024: 242 · 2023: 253 · 2022: 246 · 2021: 253 · 2020: 264 · 2019: 271 · 2018: 240 · 2017: 201 · 2016: 179 · 2015: 237 · 2014: 204 · 2013: 205 · 2012: 176
- FWCI
- 328.21
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- PubMed (publishedVersion)
- Spiral (Imperial College London) (submittedVersion)
- Funders
- Engineering and Physical Sciences Research Council (GR/R76790/02, GR/T24685/01, GR/R76790/01)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Earth and Planetary Sciences › Geology › 3D Surveying and Cultural Heritage
- Keywords
- monocular SLAM, real-time SLAM, monocular camera, 3D trajectory estimation, natural landmarks, probabilistic mapping, feature initialization, 3D localization, humanoid robots, augmented reality, handheld camera
- MeSH terms
- Algorithms, Artificial Intelligence, Computer Systems, Image Enhancement, Image Interpretation, Computer-Assisted, Numerical Analysis, Computer-Assisted, Pattern Recognition, Automated, Photogrammetry, Signal Processing, Computer-Assisted, Video Recording, Information Storage and Retrieval, Imaging, Three-Dimensional
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