Name
Abstract | ||
|---|---|---|
In robotics, a key problem is for a robot to explore its environment and use the information gathered by its sensors to jointly produce a map of its environment, together with an estimate of its position: so-called SLAM (Simultaneous Localization and Mapping) [12]. Various filtering methods --- Particle Filtering, and derived Kalman Filter methods (Extended, Unscented) --- have been applied successfully to SLAM. We present a new algorithm that adapts the Square Root Unscented Transformation [13], previously only applied to feature based maps [5], to grid mapping. We also present a new method for the so-called pose-correction step in the algorithm. Experimental results show improved computational performance on more complex grid maps compared to an existing grid based particle filtering algorithm. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-10439-8_13 | Australasian Conference on Artificial Intelligence |
Keywords | Field | DocType |
kalman filter method,existing grid,square root,grid mapping,simultaneous localization,complex grid map,so-called slam,new method,new algorithm,so-called pose-correction step,particle filtering,particle filter,simultaneous localization and mapping,kalman filter | Computer vision,Extended Kalman filter,Computer science,Particle filter,Filter (signal processing),Unscented transform,Kalman filter,Artificial intelligence,Monte Carlo localization,Simultaneous localization and mapping,Grid | Conference |
Volume | ISSN | Citations |
5866 | 0302-9743 | 1 |
PageRank | References | Authors |
0.37 | 12 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Simone Zandara | 1 | 1 | 0.71 |
| Ann E. Nicholson | 2 | 692 | 88.01 |