D.F. Wolf, G. Sukhatme, D. Fox, and W. Burgard
Autonomous Terrain Mapping and Classification Using Hidden Markov Models
Proc. of the IEEE International Conference on Robotics and Automation (ICRA)
Abstract
This paper presents a new approach for terrain mapping and
classification using mobile robots with 2D laser range finders. Our algorithm
generates 3D terrain maps and classifies navigable and non-navigable regions
on those maps using Hidden Markov models. The maps generated by our approach
can be used for path planning, navigation, local obstacle avoidance, detection
of changes in the terrain, and object recognition. We propose a map
segmentation algorithm based on Markov Random Fields, which removes small
errors in the classification. In order to validate our algorithms, we present
experimental results using two robotic platforms.
Download
Full paper [.pdf]
(607 KB)
Bibtex
@string{ICRA = "Proc. of the IEEE International Conference on Robotics and Automation (ICRA)"}
@InProceedings{wolf05icra,
TITLE = {Autonomous Terrain Mapping and Classification Using Hidden Markov Models},
AUTHOR = {Wolf, D.F. and Sukhatme, G. and Fox, D. and Burgard, W.},
BOOKTITLE = ICRA,
YEAR = {2005},
}