Motion-based mav detection in gps-denied environments

E.D. Vroon, Jim Rojer, G.C.H.E. de Croon

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Drones need to detect and localize each other if they are to collaborate in multi-robot teams or swarms. In this paper, a method based on dense optical flow (OF) is developed that detects dynamic objects. This is achieved by comparing the flow vectors with the direction to the Focus of Expansion (FoE) in the image plane. A simulation in AirSim is developed to validate this approach and to create a data set for motion-based dynamic object detection. This simulation includes ground-truth FoE, depth, OF and IMU data. The results show that our method performs well if the OF vector’s magnitude is large enough and its angle is sufficiently different from those of static world points. We expect that the presented method will serve as a useful baseline for deep learning methods using dense optical flow as input.
Original languageEnglish
Title of host publicationProceedings of the 12th International Micro Air Vehicle Conference
EditorsJose Martinez-Carranza
Number of pages7
Publication statusPublished - 2021
Event12th International Micro Air Vehicle Conference - Puebla, Mexico
Duration: 17 Nov 202119 Nov 2021
Conference number: 12


Conference12th International Micro Air Vehicle Conference
Abbreviated titleIMAV 2021


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