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Cited by 532

The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.

FrameSLAM: From Bundle Adjustment to Real-Time Visual Mapping

Konolige, K., Agrawal, M.·2008·IEEE Transactions on Robotics

Abstract

Many successful indoor mapping techniques employ frame-to-frame matching of laser scans to produce detailed local maps as well as the closing of large loops. In this paper, we propose a framework for applying the same techniques to visual imagery. We match visual frames with large numbers of point features, using classic bundle adjustment techniques from computational vision, but we keep only relative frame pose information (askeleton). The skeleton is a reduced nonlinear system that is a faithful approximation of the larger system and can be used to solve large loop closures quickly, as well as forming a backbone for data association and local registration. We illustrate the workings of the system with large outdoor datasets (10 km), showing large-scale loop closure and precise localization in real time.

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Metadata

Authors
Konolige, K., & Agrawal, M.
Venue
IEEE Transactions on Robotics
Published
2008-10
Volume
24
Issue
5
Pages
1066-1077
ISSN
  • 1552-3098 (ISSN-L)
  • 1941-0468
OpenAlex
W2121013842
Cited by
532
References
40
FWCI
71.09
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: FrameSLAM: From Bundle Adjustment to Real-Time Visual Mapping. https://app.cochat.ai/papers/10.1109/tro.2008.2004832
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Konolige, K., & Agrawal, M. (2008). FrameSLAM: From Bundle Adjustment to Real-Time Visual Mapping. IEEE Transactions on Robotics. https://doi.org/10.1109/tro.2008.2004832

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