Abstract
With the primary goal of enhancing the efficiency of drones for research and rescue missions through the exploitation of neuromorphic sensors and event-based vision, our focus in this work lies in setting up a simulated environment that can be used for synthetic data generation. In particular, we employ Unreal Engine to generate scenes suitable for the case of vehicle perception, followed by a dynamic event-based simulation environment in interaction with the AirSim and v2e software tools. The synthetic event data acquired in this simulated environment is shown to provide a valuable resource for training Artificial Intelligence (AI) systems and more particularly for the task of car detection using YOLOv7.
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Acknowledgement
This work has received a French government support granted to the Labex CominLabs excellence laboratory and managed by the National Research Agency in the “Investing for the Future” program under reference ANR-10-LABX-07-01 from September 2022 to December 2024. In this program, the project associated to this work is LEASARD (Low-Energy deep neural networks for Autonomous Search-And-Rescue Drones).
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Amessegher, I., Fradi, H., Liard, C., Diguet, JP., Papadakis, P., Arzel, M. (2024). Simulating Aerial Event-Based Environment: Application to Car Detection. In: Secchi, C., Marconi, L. (eds) European Robotics Forum 2024. ERF 2024. Springer Proceedings in Advanced Robotics, vol 32. Springer, Cham. https://doi.org/10.1007/978-3-031-76424-0_26
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DOI: https://doi.org/10.1007/978-3-031-76424-0_26
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