DocumentCode :
22867
Title :
A Quadratic-Complexity Observability-Constrained Unscented Kalman Filter for SLAM
Author :
Huang, Guoquan P. ; Mourikis, Anastasios I. ; Roumeliotis, Stergios I.
Author_Institution :
Comput. Sci. & Artificial Intell. Lab., Massachusetts Inst. of Technol., Cambridge, MA, USA
Volume :
29
Issue :
5
fYear :
2013
fDate :
Oct. 2013
Firstpage :
1226
Lastpage :
1243
Abstract :
This paper addresses two key limitations of the unscented Kalman filter (UKF) when applied to the simultaneous localization and mapping (SLAM) problem: the cubic computational complexity in the number of states and the inconsistency of the state estimates. To address the first issue, we introduce a new sampling strategy for the UKF, which has constant computational complexity. As a result, the overall computational complexity of UKF-based SLAM becomes of the same order as that of the extended Kalman filter (EKF)-based SLAM, i.e., quadratic in the size of the state vector. Furthermore, we investigate the inconsistency issue by analyzing the observability properties of the linear-regression-based model employed by the UKF. Based on this analysis, we propose a new algorithm, termed observability-constrained (OC)-UKF, which ensures the unobservable subspace of the UKF´s linear-regression-based system model is of the same dimension as that of the nonlinear SLAM system. This results in substantial improvement in the accuracy and consistency of the state estimates. The superior performance of the OC-UKF over other state-of-the-art SLAM algorithms is validated by both Monte-Carlo simulations and real-world experiments.
Keywords :
Kalman filters; SLAM (robots); computational complexity; path planning; regression analysis; state estimation; EKF-based SLAM; Monte-Carlo simulations; SLAM; UKF; cubic computational complexity; extended Kalman filter-based SLAM; nonlinear SLAM system; quadratic-complexity observability-constrained unscented Kalman filter; simultaneous localization and mapping problem; state estimation; Computational complexity; estimator consistency; simultaneous localization and mapping (SLAM); system observability; unscented Kalman filter;
fLanguage :
English
Journal_Title :
Robotics, IEEE Transactions on
Publisher :
ieee
ISSN :
1552-3098
Type :
jour
DOI :
10.1109/TRO.2013.2267991
Filename :
6553094
Link To Document :
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