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Abstract
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| Obstacle avoidance is a key feature for safe Unmanned Aerial Vehicle (UAV) navigation. While solutions have been proposed for static obstacle avoidance, systems enabling avoidance of dynamic objects, such as drones, are hard to implement due to the detection range and ?eld-of-view (FOV) requirements, as well as the constraints for integrating such systems on-board small UAVs. In this work, a dataset of 6k synthetic depth maps of drones has been generated and used to train a state-of-the-art deep learning-based drone detection model. While many sensing technologies can only provide relative altitude and azimuth of an obstacle, our depth mapbased approachenables full 3Dlocalization of theobstacle. This is extremely useful for collision avoidance, as 3D localization of detected drones is key to perform ef?cient collision-free path planning. The proposed detection technique has been validated in several real depth map sequences, with multiple types of drones ?ying at up to 2 m/s, achieving an average precision of 98.7%, an average recall of 74.7% and a record detection range of 9.5 meters. | |
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International
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Si |
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Congress
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International Conference on Intelligent Robots and Systems (IROS) |
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960 |
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Place
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Reviewers
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Si |
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ISBN/ISSN
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Start Date
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End Date
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From page
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1034 |
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To page
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1037 |
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Drone detection using depth maps |