Development of an autonomous RC-car

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. © 2013 Springer-Verlag Berlin Heidelberg.