Autonomous Terrain Mapping
and Classification Using Hidden Markov Models
Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2005
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.