Vision-based road direction detection for autonomous Unmanned Ground Vehicles (UGVs)

Who: Peyman Moghadam, ICT Centre CSIRO
When: September 27th, 2pm
Where:  ALCOR lab @DIIAG, via Ariosto 25, Room A3, Basement

Abstract: We present a novel approach for vision-based road direction detection for autonomous Unmanned Ground Vehicles (UGVs). The proposed method utilizes only monocular vision information similar to human perception to detect road directions with respect to the vehicle. The algorithm searches for a global feature of the roads due to perspective projection (so-called vanishing point) to distinguish road directions. The proposed approach consists of two stages. The first stage estimates the vanishing-point locations from single frames. The second stage uses a Rao-Blackwellised particle filter to track initial vanishing-point estimations over a sequence of images in order to provide more robust estimation. Simultaneously, the direction of the road ahead of the vehicle is predicted, which is prerequisite information for vehicle steering and path planning. The proposed approach assumes minimum prior knowledge about the environment and can cope with complex situations such as ground cover variations, different illuminations, and cast shadows. Its performance is evaluated on video sequences taken during test run of the DARPA Grand Challenge.
Dr. Peyman Mo Postdoctoral Fellow Autonomous Systems Laboratory ICT Centre CSIRO

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