Robotics: Science and Systems IV

Detection of Principle Directions in Unknown Environments for Autonomous Navigation

Dmitri Dolgov, Sebastian Thrun

Abstract: Autonomous navigation in unknown but well-structured environments (e.g., parking lots) is a common task for human drivers and an important goal for autonomous vehicles. In such environments, the vehicles must obey the standard conventions of driving (e.g., passing oncoming vehicles on the correct side), but often lack a map that can be used to guide path planning in an appropriate way. The robots must therefore rely on features of the environment to drive in a safe and predictable way. In this work we focus on detecting one of such features, the principle directions of the environment, and demonstrate how it can be used in path planning. We propose a Markov-random-field (MRF) model for estimating the maximum likelihood field of principle directions, given the local linear features extracted from the vehicle's sensor data, and show that the method leads to robust estimates of principle directions in complex real-life driving environments. We also demonstrate how the computed principle directions can be used to guide a path-planning algorithm, leading to the generation of significantly improved trajectories.



    AUTHOR    = {Dmitri Dolgov, Sebastian Thrun},
    TITLE     = {Detection of Principle Directions in Unknown Environments for Autonomous Navigation},
    BOOKTITLE = {Proceedings of Robotics: Science and Systems IV},
    YEAR      = {2008},
    ADDRESS   = {Zurich, Switzerland},
    MONTH     = {June},
    DOI       = {10.15607/RSS.2008.IV.010}