Name
Abstract | ||
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In this paper we describe Divide and Conquer SLAM (D&C SLAM), an algorithm for performing Simulta- neous Localization and Mapping using the Extended Kalman Filter. D&C SLAM overcomes the two fundamental limitations of standard EKF SLAM: 1- the computational cost per step is reduced from O(n2) to O(n) (the cost full SLAM is reduced from O(n3) to O(n2)); 2- the resulting vehicle and map estimates have better consistency properties than standard EKF SLAM in the sense that the computed state covariance adequately represents the real error in the estimation. Unlike many current large scale EKF SLAM techniques, this algorithm computes an exact solution, without relying on approximations or simplifications to reduce computational complexity. Also, estimates and covariances are available when needed by data association without any further computation. Empirical results show that, as a bi-product of reduced computations, and with- out losing precision because of approximations, D&C SLAM has better consistency properties than standard EKF SLAM. Both characteristics allow to extend the range of environments that can be mapped in real time using EKF. We describe the algorithm and study its computational cost and consistency properties. I. INTRODUCTION Simultaneous Localization and Mapping (SLAM) consists in building a map of an unknown environment by traversing it using a vehicle with an onboard sensor, while simultaneously determining the vehicle location within the map. In the Extended Kalman Filter solution to SLAM (EKF SLAM), this problem is stated as a stochastic estimation process, in which a move-sense-update cycle is carried out. At every step, the EKF is used to obtain the state vector estimatex containing the vehicle pose and n feature locations, along with the estimated error covariance matrix P. The EKF solution to SLAM has been used successfully |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/ROBOT.2007.363561 | Roma |
Keywords | Field | DocType |
Kalman filters,SLAM (robots),computational complexity,covariance matrices,divide and conquer methods,computational complexity,data association,divide-and-conquer SLAM,extended Kalman filter,simultaneous localization and mapping,state covariance | Mathematical optimization,Extended Kalman filter,Control theory,Algorithm,Kalman filter,Covariance matrix,Divide and conquer algorithms,Simultaneous localization and mapping,Sparse matrix,Mathematics,Covariance,Computational complexity theory | Conference |
Volume | Issue | ISSN |
2007 | 1 | 1050-4729 E-ISBN : 1-4244-0602-1 |
ISBN | Citations | PageRank |
1-4244-0602-1 | 22 | 1.26 |
References | Authors | |
12 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lina María Paz | 1 | 86 | 8.88 |
| Patric Jensfelt | 2 | 22 | 1.26 |
| Juan Domingo | 3 | 3319 | 258.54 |
| José Neira | 4 | 1266 | 136.94 |