Comparison of Different Approaches to Vibration-based Terrain Classification
Christian Weiß, Nikolas Fechner, Matthias Stark, Andreas Zell
- Year
- 2008
- Citations
- 46
Abstract
Abstract — There is a variety of different terrain types in outdoor environments, each posing different dangers to the robot and demanding a different driving style. In a previous paper, we presented a terrain classification method based on Support Vector Machines (SVM), which uses vibrations induced in the body of the robot to learn different terrain classes. However, in the previous paper, our experimental results were based on vibration data collected by a hand-pulled cart with relatively hard wheels. In this paper, we present experiments on data collected by our RWI ATRV-Jr outdoor robot. Additionally, we compare our SVM-based method to alternative classification methods. The comparison shows that our approach outperforms the other methods. Index Terms — Outdoor robotics, vibration-based terrain classification I.
Keywords
Related papers
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Fractional Differential Equations
Igor Podlubný
2025
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991