An O(N²) Square Root Unscented Kalman Filter for Visual Simultaneous Localization and Mapping

This paper develops a square root unscented Kalman filter (SRUKF) for performing video-rate visual simultaneous localization and mapping (SLAM) using a single camera. The conventional UKF has been proposed previously for SLAM, improving the handling of nonlinearities compared with the more widely us...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2009-07, Vol.31 (7), p.1251-1263
Main Authors: Holmes, S.A., Klein, G., Murray, D.W.
Format: Article
Language:English
Subjects:
Algorithmics. Computability. Computer arithmetics
Algorithms
Applied sciences
Artificial Intelligence
Cameras
Complexity
Computer science
control theory
systems
Cost function
Covariance matrix
Exact sciences and technology
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Imaging, Three-Dimensional - methods
Kalman filters
Layout
Localization
Mapping
Motion
Motion pictures
Navigation
Packaging
Pattern Recognition, Automated - methods
Pattern recognition. Digital image processing. Computational geometry
Position (location)
Reproducibility of Results
Roots
Sensitivity and Specificity
Signal Processing, Computer-Assisted
Simultaneous localization and mapping
State estimation
Structure from motion
Studies
Subtraction Technique
Target tracking
Theoretical computing
unscented Kalman filter
Video Recording - methods
Visual
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Summary:This paper develops a square root unscented Kalman filter (SRUKF) for performing video-rate visual simultaneous localization and mapping (SLAM) using a single camera. The conventional UKF has been proposed previously for SLAM, improving the handling of nonlinearities compared with the more widely used extended Kalman filter (EKF). However, no account was taken of the comparative complexity of the algorithms: In SLAM, the UKF scales as O ( N 3 ) in the state length, compared to the EKF's O ( N 2 ), making it unsuitable for video-rate applications with other than unrealistically few scene points. Here, it is shown that the SRUKF provides the same results as the UKF to within machine accuracy and that it can be reposed with complexity O ( N 2 ) for state estimation in visual SLAM. This paper presents results from video-rate experiments on live imagery. Trials using synthesized data show that the consistency of the SRUKF is routinely better than that of the EKF, but that its overall cost settles at an order of magnitude greater than the EKF for large scenes.
ISSN:0162-8828
1939-3539
DOI:10.1109/TPAMI.2008.189