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Title

Onboard Detection and Localization of Drones Using Depth Maps

AuthorsCarrio, Adrián; Tordesillas, Jesús; Vemprala, Sai; Saripalli, Srikanth; Campoy, Pascual ; How, Jonathan P.
KeywordsDrone
Detection
Collision avoidance
Depth map
Three-dimensional displays
Autonomous aerial vehicles
Issue Date5-Feb-2020
PublisherInstitute of Electrical and Electronics Engineers
CitationIEEE Access 8: 30480 - 30490 (2020)
AbstractObstacle avoidance is a key feature for safe drone navigation. While solutions are already commercially available for static obstacle avoidance, systems enabling avoidance of dynamic objects, such as drones, are much harder to develop due to the efficient perception, planning and control capabilities required, particularly in small drones with constrained takeoff weights. For reasonable performance, obstacle detection systems should be capable of running in real-time, with sufficient field-of-view (FOV) and detection range, and ideally providing relative position estimates of potential obstacles. In this work, we achieve all of these requirements by proposing a novel strategy to perform onboard drone detection and localization using depth maps. We integrate it on a small quadrotor, thoroughly evaluate its performance through several flight experiments, and demonstrate its capability to simultaneously detect and localize drones of different sizes and shapes. In particular, our stereo-based approach runs onboard a small drone at 16 Hz, detecting drones at a maximum distance of 8 meters, with a maximum error of 10% of the distance and at relative speeds up to 2.3 m/s. The approach is directly applicable to other 3D sensing technologies with higher range and accuracy, such as 3D LIDAR.
DescriptionSpecial thanks to B. Lopez (ACL-MIT) and A. Ripoll (TU Delft) for their contributions in the early stages of this work. They would also like to thank J. Yuan (MIT) for his help in the experiments and P. Tordesillas for his help with the figures in the article.
Publisher version (URL)https://doi.org/10.1109/ACCESS.2020.2971938
URIhttp://hdl.handle.net/10261/220702
DOIhttp://dx.doi.org/10.1109/ACCESS.2020.2971938
E-ISSN2169-3536
Appears in Collections:(CAR) Artículos
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