This paper describes a system for detecting and estimating the properties of multiple travel lanes in an urban road network from calibrated video imagery and laser range data acquired by a moving vehicle. The system operates in several stages on multiple processors, fusing detected road markings, obstacles, and curbs into a stable non-parametric estimate of nearby travel lanes. The system incorporates elements of a provided piecewise-linear road network as a weak prior. The authors' method is notable in several respects: it estimates multiple travel lanes; it fuses asynchronous, heterogeneous sensor streams; it handles high-curvature roads; and it makes no assumption about the position or orientation of the vehicle with respect to the road. They analyze the system's performance in the context of the 2007 DARPA Urban Challenge. With five cameras and thirteen lidars, it was incorporated into a closed-loop controller to successfully guide an autonomous vehicle through a 90 km urban course at speeds up to 40 km/h amidst moving traffic. The primary contributions of this paper are: A method for estimating multiple lanes of travel in a typical urban road network using only information from local sensors; - A method for fusing these estimates with a weak prior, such as that derived from a topological road map with sparse metrical information; - Methods for using monocular cameras to detect road markings; - Multi-sensor fusion algorithms combining information from video and lidar sensors. The authors also describe their method's failure modes, and possible directions for future work.


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    Title :

    Multi-sensor lane finding in urban road networks


    Additional title:

    Multisensor-Spurauffinden im Stadtstraßennetz


    Contributors:


    Publication date :

    2009


    Size :

    8 Seiten, 10 Bilder, 1 Tabelle, 18 Quellen



    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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