Abstract

We present a novel and general optimisation framework for visual SLAM, which scales for both local, highly accu-rate reconstruction and large-scale motion with long loop closures. We take a two-level approach that combines accu-rate pose-point constraints in the primary region of interest with a stabilising periphery of pose-pose soft constraints. Our algorithm automatically builds a suitable connected graph of keyposes and constraints, dynamically selects in-ner and outer window membership and optimises both si-multaneously. We demonstrate in extensive simulation ex-periments that our method approaches the accuracy of off-line bundle adjustment while maintaining constant-time op-eration, even in the hard case of very loopy monocular cam-era motion. Furthermore, we present a set of real experi-ments for various types of visual sensor and motion, includ-ing large scale SLAM with both monocular and stereo cam-eras, loopy local browsing with either monocular or RGB-D cameras, and dense RGB-D object model building. 1.

Keywords

Computer visionArtificial intelligenceMonocularSimultaneous localization and mappingComputer scienceBundle adjustmentRGB color modelWindow (computing)Scale (ratio)RobotMobile robotImage (mathematics)

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Publication Info

Year
2011
Type
article
Citations
287
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Closed

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287
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Cite This

Hauke Strasdat, Andrew J. Davison, J. M. M. Montiel et al. (2011). Double window optimisation for constant time visual SLAM. 2011 International Conference on Computer Vision . https://doi.org/10.1109/iccv.2011.6126517

Identifiers

DOI
10.1109/iccv.2011.6126517

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Data completeness: 81%