Name
Abstract | ||
|---|---|---|
In this paper we show that all processes associated to the move-sense-update cycle of EKF SLAM can be carried out in time linear in the number of map features. We describe Divide and Conquer SLAM, an EKF SLAM algorithm where the computational complexity per step is reduced from O(n2) to O(n) (the total cost of SLAM is reduced from O(n3) to O(n2)). In addition, the resulting vehicle and map estimates have better consistency properties than standard EKF SLAM in the sense that the computed state covariance more adequately represents the real error in the estimation. Both simulated experiments and the Victoria Park Dataset are used to provide evidence of the advantages of this algorithm. Index Terms—SLAM, Computational Complexity, Consistency, Linear Time. |
Year | Venue | Keywords |
|---|---|---|
2007 | Robotics: Science and Systems | computational complexity,linear time,simulation experiment,divide and conquer,indexing terms |
Field | DocType | Citations |
Extended Kalman filter,Computer science,Algorithm,Theoretical computer science,Data association,Artificial intelligence,Divide and conquer algorithms,Simultaneous localization and mapping,Machine learning,Computational complexity theory,Covariance | Conference | 7 |
PageRank | References | Authors |
0.89 | 21 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lina María Paz | 1 | 86 | 8.88 |
| José E. Guivant | 2 | 751 | 103.39 |
| Juan Domingo | 3 | 3319 | 258.54 |
| José Neira | 4 | 253 | 12.60 |