DocumentCode :
3188769
Title :
Optimal local map size for EKF-based SLAM
Author :
Paz, Lina M. ; Neira, Jose
Author_Institution :
Dept. of Comput. Sci., Zaragoza Univ.
fYear :
2006
fDate :
9-15 Oct. 2006
Firstpage :
5019
Lastpage :
5025
Abstract :
In this paper we show how to optimize the computational cost and maximize consistency in EKF-based SLAM for large environments. We combine local mapping with map joining in a way that the total cost of computing the final map is minimized compared to full global EKF-SLAM. This solution is not now only shown to be (1) computationally optimal, but in addition, it is empirically shown that (2) it also produces the most consistent environment map. For a given environment size and sensor range, we can determine the optimal size of the local maps required to minimize the total computational cost and maximize map consistency. The motivation of this work is described in a map building experiment in our lab, and the statistical significance of the proposed method is validated using Monte Carlo simulations
Keywords :
Kalman filters; Monte Carlo methods; SLAM (robots); nonlinear filters; path planning; EKF-based SLAM; Monte Carlo simulations; local mapping with map joining; maximize map consistency; optimal local map size; Computational efficiency; Computer science; Costs; Covariance matrix; Intelligent robots; Sampling methods; Simultaneous localization and mapping; Sparse matrices; Uncertainty; Vehicles; Computational Cost; EKF SLAM; Local Mapping; Map Consistency; Map Joining;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on
Conference_Location :
Beijing
Print_ISBN :
1-4244-0258-1
Electronic_ISBN :
1-4244-0259-X
Type :
conf
DOI :
10.1109/IROS.2006.282529
Filename :
4059217
Link To Document :
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