Abstract
This paper describes a method of estimating road region and ego-motion for outdoor mobile robots. In outdoor navigation, we have to cope with various road scenes where, for example, road boundary features such as white lines and curbs may not be always available. Integration of sensor data from multiple sensors is thus effective for realizing a robust road region estimation. Since sensor data are obtained as a robot moves, and since an odometry-based dead reckoning suffers from accumulating errors, we develop a method which simultaneously estimates road region and robot ego-motion. We implement the method using a particle filter. The method also has a mechanism of periodically generating new particles using multiple road models to cope with gradual road shape changes. The proposed method has been successfully applied to autonomous navigation in various road scenes.
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© 2009 Springer-Verlag Berlin Heidelberg
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Matsushita, Y., Miura, J. (2009). Simultaneous Estimation of Road Region and Ego-Motion with Multiple Road Models. In: Hahn, H., Ko, H., Lee, S. (eds) Multisensor Fusion and Integration for Intelligent Systems. Lecture Notes in Electrical Engineering, vol 35. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89859-7_19
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DOI: https://doi.org/10.1007/978-3-540-89859-7_19
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