Abstract
In this paper we describe how visual features can be incorporated into the well known tentacles approach [1] which up to now has only used LIDAR and GPS data and was therefore limited to scenarios with significant obstacles or non-flat surfaces along roads. In addition we present a visual feature considering only color intensity which can be used to visually rate tentacles. The presented sensor fusion and color based feature were both applied with great success at the C-ELROB 2009 robotic competition.
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References
F. von Hundeishausen, M. Himmelsbach, F. Hecker, A. Müller, and H.-J. Wünsche, “Driving with Tentacles — integral structures of sensing and motion,” International Journal of Field Robotics Research, 2008.
C. Rasmussen, “Combining laser range, color, and texture cues for autonomous road following,” IEEE Inter. Conf. on Robotics and Automation, 2002.
A. Broggi, S. Cattani, P. P. Porta, and P. Zani, “A laserscannervision fusion system implemented on the terramax autonomous vehicle,” IEEE Int. Conf. on Intelligent Robots and Systems, 2006.
U. Franke, H. Loose, and C. Knöppel, “Lane Recognition on Country Roads,” Intelligent Vehicles Symposium, pp. 99–104, 2007.
S. Thrun, M. Montemerlo, and A. Aron, “Probabilistic terrain analysis for highspeed desert driving,” in Proceedings of Robotics: Science and Systems, Philadelphia, USA, August 2006.
C. Tan, T. Hong, T. Chang, and M. Shneier, “Color model-based real-time learning for road following,” IEEE Intelligent Transportation Systems Conference, 2006.
J. Zhang and H.-H. Nagel, “Texture-based segmentation of road images,” IEEE Symposium on Intelligent Vehicles, pp. 260–265, 1994.
E. D. Dickmanns and B. D. Mysliwetz, “Recursive 3-d road and relative ego-state recognition,” IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 14, pp. 199–213, February 1992.
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© 2009 Springer-Verlag Berlin Heidelberg
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Manz, M., Himmelsbach, M., Luettel, T., Wuensche, HJ. (2009). Fusing LIDAR and Vision for Autonomous Dirt Road Following. In: Dillmann, R., Beyerer, J., Stiller, C., Zöllner, J.M., Gindele, T. (eds) Autonome Mobile Systeme 2009. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10284-4_3
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DOI: https://doi.org/10.1007/978-3-642-10284-4_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10283-7
Online ISBN: 978-3-642-10284-4
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