Abstract
When an outdoor mobile robot traverses different types of ground surfaces, different types of vibrations are induced in the body of the robot. These vibrations can be used to learn a discrimination between different surfaces and to classify the current terrain. Recently, we presented a method that uses Support Vector Machines for classification, and we showed results on data collected with a hand-pulled cart. In this paper, we show that our approach also works well on an outdoor robot. Furthermore, we more closely investigate in which direction the vibration should be measured. Finally, we present a simple but effective method to improve the classification by combining measurements taken in multiple directions.
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References
Wilcox B. H.: Non-Geometric Hazard Detection for a Mars Microrover. Proc. AIAA Conf. on Intelligent Robots in Field, Factory, Service, Space. Houston, TX, pp. 675–684, 1994
Iagnemma K., Dubowsky S.: Terrain Estimation for High-Speed Rough-Terrain Autonomous Vehicle Navigation. Proc. SPIE Conf. on Unmanned Ground Vehicle Technology IV, 2002
Brooks C. A., Iagnemma K.: Vibration-Based Terrain Classification for Planetary Exploration Rovers. IEEE Trans. on Robotics, 21(6), pp. 1185–1191, 2005
Sadhukhan D., Moore C: Online Terrain Estimation Using Internal Sensors. Proc. Florida Conf. on Recent Advances in Robotics, Boca Raton, FL, 2003
Sadhukhan D.: Autonomous Ground Vehicle Terrain Classification Using Internal Sensors. Masters Thesis, Dept. Mech. Eng., Florida State University, 2004
Weiss C., Fröhlich H., Zell, A.: Vibration-based Terrain Classification Using Support Vector Machines. Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, Beijing, China, pp. 4429–4434, 2006
Stavens D., Hoffmann G., Thrun S.: Online Speed Adaptation Using Supervised Learning for High-Speed, Off-Road Autonomous Driving. Proc. Int. Joint Conf. on Artificial Intelligence, Hyderabad, India, 2007
Cortes C, Vapnik V.: Support Vector Networks. Machine Learning, 20, pp. 273–297, 1995
Schölkopf B., Smola A. J.: Learning with Kernels. MIT Press, Cambridge, MA, 2002
Chang C. C, Lin C. J.: LIBSVM: A Library for Support Vector Machines. Software available at http://www.csie.ntu.edu.tw/∼cjlin/libsvm, 2001
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© 2007 Springer-Verlag Berlin Heidelberg
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Weiss, C., Stark, M., Zell, A. (2007). SVMs for Vibration-Based Terrain Classification. In: Berns, K., Luksch, T. (eds) Autonome Mobile Systeme 2007. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74764-2_1
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DOI: https://doi.org/10.1007/978-3-540-74764-2_1
Publisher Name: Springer, Berlin, Heidelberg
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Online ISBN: 978-3-540-74764-2
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