DocumentCode :
399459
Title :
The stability of covariance inflation methods for SLAM
Author :
Julier, Simon J.
Author_Institution :
IDAK Ind., Jefferson City, MO, USA
Volume :
3
fYear :
2003
fDate :
27-31 Oct. 2003
Firstpage :
2749
Abstract :
This paper analyses the consequences of using Covariance Inflation Methods for Simultaneous Localisation and Map Building (SLAM). Covariance Inflation refers to the process of adding a positive semidefinite matrix to the system covariance matrix to improve the properties of a SLAM algorithm. Because this approach can be used to decorrelate the state estimates in the covariance matrix, it has the potential to greatly reduce both computational and storage costs. However, it also raises the risk that the covariance can increase without bound. This paper analyses the properties of covariance inflation algorithms to assess their impact on performance. We prove that, to prevent the steady-state covariance from being increased, the computational and storage costs must be linear in the number of beacons. Furthermore, if the steady-state covariance is to remain finite, the inflation method cannot impose structures on the filter which are continually broken down. These results are illustrated in a simple linear example.
Keywords :
covariance matrices; decorrelation; mobile robots; stability; state estimation; Map Building; Simultaneous Localisation; computational cost; covariance inflation algorithms; covariance inflation methods; semidefinite matrix; steady state covariance; storage cost; system covariance matrix; Algorithm design and analysis; Computational efficiency; Covariance matrix; Decorrelation; Filters; Performance analysis; Simultaneous localization and mapping; Stability; State estimation; Steady-state;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Robots and Systems, 2003. (IROS 2003). Proceedings. 2003 IEEE/RSJ International Conference on
Print_ISBN :
0-7803-7860-1
Type :
conf
DOI :
10.1109/IROS.2003.1249286
Filename :
1249286
Link To Document :
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