Name
Abstract | ||
|---|---|---|
Outdoor environments bear the problem of different terrains along with changing driving properties. Therefore, compared to
indoor environments, the kinematics of mobile robots is much more complex. In this paper we present a comprehensive approach
to learn the function of outdoor kinematics for mobile robots. Future robot positions are estimated by employing Gaussian
process regression (GPR) in combination with an Unscented Kalman filter (UKF). Our approach uses optimized terrain models
according to the classification of the current terrain – accomplished through Gaussian process classification (GPC) and a
second order Bayesian filter (BF). Experiments showed our approach to provide more accurate estimates compared to single terrain
model methods, as well as to be competitive to other dynamic approaches.
|
Year | DOI | Keywords |
|---|---|---|
2010 | 10.1007/978-3-642-19539-6_10 | gaussian process,mobile robots,machine learning.,position estimation,terrain classification |
Field | DocType | Citations |
Kriging,Computer vision,Kinematics,Computer science,Terrain,Kalman filter,Artificial intelligence,Gaussian process,Robot,Raised-relief map,Mobile robot | Conference | 0 |
PageRank | References | Authors |
0.34 | 18 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Brunner | 1 | 17 | 3.30 |
| Dirk Schulz | 2 | 1701 | 236.54 |
| Armin B. Cremers | 3 | 2287 | 446.79 |