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Bundle Adjustment — A Modern Synthesis
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“These are commonly referred to as landmark-based or feature-based representations, and have been widespread in mobile robotics since early work on localization and mapping, and in computer vision in the context of Structure from Motion [2, 244].”
From Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016) · cites this paper as [244]
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Metadata
- Authors
- Triggs, B., McLauchlan, P. F., Hartley, R. I., & Fitzgibbon, A. W.
- Affiliations
- Institut national de recherche en sciences et technologies du numérique (FR)
- University of Surrey (GB)
- General Electric (United States) (US)
- University of Oxford (GB)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2000
- Volume
- 1883
- Pages
- 298-372
- ISSN
- 0302-9743 (ISSN-L)
- OpenAlex
- W2124313187
- Cited by
- 3,988
- References
- 101
- Citations by year
- 2026: 79 · 2025: 137 · 2024: 196 · 2023: 190 · 2022: 201 · 2021: 236 · 2020: 231 · 2019: 239 · 2018: 204 · 2017: 222 · 2016: 178 · 2015: 220 · 2014: 194 · 2013: 187 · 2012: 189
- FWCI
- 41.85
- Type
- conference paper
- Language
- en
- Open access
- green
- inria.hal.science
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- HAL (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
- Physical Sciences › Earth and Planetary Sciences › Geology › 3D Surveying and Cultural Heritage
- Keywords
- bundle adjustment, photogrammetry, structure from motion, visual reconstruction, robust cost function, recursive optimization, gauge invariance, quality control, nonlinear least squares
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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