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Learning to Fly by MySelf: A Self-Supervised CNN-Based Approach for Autonomous Navigation
2018
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Nowadays, Unmanned Aerial Vehicles (UAVs) are becoming increasingly popular facilitated by their extensive availability. Autonomous navigation methods can act as an enabler for the safe deployment of drones on a wide range of real-world civilian applications. In this work, we introduce a self-supervised CNN-based approach for indoor robot navigation. Our method addresses the problem of real-time obstacle avoidance, by employing a regression CNN that predicts the agent's distance-to-collision in
doi:10.1109/iros.2018.8594204
dblp:conf/iros/KourisB18
fatcat:hbnttqeklzfitamruim2ax5wgm