Micro air vehicles which operate autonomously at low altitude in cluttered environments require a method for on-board obstacle avoidance for safe operation. Prior approaches can be divided between purely reactive approaches, mapping low-level visual features directly to headings to maneuver the vehicle around the obstacle, and deliberative methods that use on-board 3-D sensors to create a 3-D, voxel-based world model, which is then used to generate collision free 3-D trajectories. In this paper, we use forward-looking stereo vision with a large horizontal and vertical field of view and project range from stereo into a novel robot-centered, cylindrical, inverse range map we call an egocylinder. With this implementation we reduce the complexity of our world representation from a 3D map to a 2.5D image space representation, which supports very efficient motion planning and collision-checking. Configuration space expansion is done very efficiently on the egocylinder as an image processing function. Deploying a fast reactive motion planner directly on the configuration space expanded egocylinder image, we demonstrate the effectiveness of this new approach experimentally in an indoor environment.


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    Title :

    Stereo Vision-Based Obstacle Avoidance for Micro Air Vehicles Using an Egocylindrical Image Space Represntation


    Contributors:
    Brockers, R. (author) / Fragoso, A. (author) / Matthies, L. (author)

    Conference:

    SPIE Defense and Commercial Sensing ; 2016 ; Baltimore, MD, United States


    Publication date :

    2016-04-17


    Type of media :

    Conference paper


    Type of material :

    No indication


    Language :

    English






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