Moving obstacle avoidance is a fundamental requirement for any robot operating in real environments, where pedestrians, bicycles, and cars are present. In this paper, we propose and validate a framework for avoiding moving obstacles during visual navigation with a wheeled mobile robot. Visual navigation consists of following a path, represented as an ordered set of key images, which have been acquired by an on-board camera in a teaching phase. While following such a path, our robot is able to avoid static and moving obstacles, which were not present during teaching, and which are sensed by an on-board lidar. The proposed approach takes explicitly into account obstacle velocities, estimated using an appropriate Kalman-based observer. The velocities are then used to predict the obstacle positions within a tentacle-based approach. Finally, our approach is validated in a series of real outdoor experiments, showing that when the obstacle velocities are considered, the robot behavior is safer, smoother, and faster than when it is not.


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    Title :

    Autonomous Visual Navigation and Laser-Based Moving Obstacle Avoidance


    Contributors:


    Publication date :

    2014-10-01


    Size :

    1126063 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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