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An O(N(2)) square root unscented Kalman Filter for visual simultaneous localization and mapping.

Abstract:

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...

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Publication status:
Published

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Publisher copy:
10.1109/tpami.2008.189

Authors


Holmes, SA More by this author
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Institution:
University of Oxford
Department:
Oxford, MPLS, Engineering Science
Journal:
IEEE transactions on pattern analysis and machine intelligence
Volume:
31
Issue:
7
Pages:
1251-1263
Publication date:
2009-07-05
DOI:
EISSN:
1939-3539
ISSN:
0162-8828
URN:
uuid:e77619c6-1e71-450f-a2d7-4300686a5976
Source identifiers:
62594
Local pid:
pubs:62594

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