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Autonomous Visual Navigation and Laser-Based Moving Obstacle Avoidance
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DOI：10.1109/TITS.2014.2308977.png)
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Abstract
En 中文
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.
Keywords:
Collision avoidance
visual navigation
visual servoing
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