Abstract
In this paper we tackle the development of a robotic-car with hardware control, lane detection, mapping, localization and path planning capabilities. We aim for a completely independent, reliable and robust system that can traverse a single lane track bordered by white lines on an optimal path. To detect the track boundaries, we implement two different approaches. A RANSAC approach, which approximates the lines by random sampling of splines, and a polyline approach, which applies primitive image processing in combination with a road model. To map the environment, odometry and vision-based information is fused by a particle filter based Simultaneous Localization and Mapping system. The map is afterwards used in conjunction with Adaptive Monte Carlo Localization. For path planning, a one step continuous-curvature approach based on sensor or maps data is used. To offer more detailed information about the environment, we introduce a generic map analysis system. It is employed to evaluate the efficiency of certain paths on the track.
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Claes, D., Fossel, J., Broecker, B., Hennes, D., Tuyls, K. (2013). Development of an Autonomous RC-car. In: Lee, J., Lee, M.C., Liu, H., Ryu, JH. (eds) Intelligent Robotics and Applications. ICIRA 2013. Lecture Notes in Computer Science(), vol 8103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40849-6_10
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DOI: https://doi.org/10.1007/978-3-642-40849-6_10
Publisher Name: Springer, Berlin, Heidelberg
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