Robust and accurate road map inference

Over the last ten years, electronic vehicle guidance systems have become increasingly popular. However, their performance is subject to the availability and accuracy of digital road maps. Most current digital maps are still inadequate for advanced applications in unstructured environments. Lack of detailed up-to-date information and insufficient accuracy and refinement of the road geometry are among the most important shortcomings. The massive use of inexpensive GPS receivers, combined with the rapidly increasing availability of wireless communication infrastructure, suggests that large volumes of data combining both modalities will be available in a near future. The approach presented here draws on machine learning techniques to process logs of position traces to consistently build a detailed and accurate representation of the road network and, more importantly, extract the actual paths followed by vehicles. Experimental results with data from large mining operations are presented to validate the algorithm.