High-Fidelity Drone Simulation with Depth Camera Noise and Improved Air Drag Force Models
| Autor: | Woosung Kim, Tuan Luong, Yoonwoo Ha, Myeongyun Doh, Juan Fernando Medrano Yax, Hyungpil Moon |
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| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Applied Sciences, Vol 13, Iss 19, p 10631 (2023) |
| Druh dokumentu: | article |
| ISSN: | 13191063 2076-3417 |
| DOI: | 10.3390/app131910631 |
| Popis: | Drone simulations offer a safe environment for collecting data and testing algorithms. However, the depth camera sensor in the simulation provides exact depth values without error, which can result in variations in algorithm behavior, especially in the case of SLAM, when transitioning to real-world environments. The aerodynamic model in the simulation also differs from reality, leading to larger errors in drag force calculations at high speeds. This disparity between simulation and real-world conditions poses challenges when attempting to transfer high-speed drone algorithms developed in the simulated environment to actual operational settings. In this paper, we propose a more realistic simulation by implementing a novel depth camera noise model and an improved aerodynamic drag force model. Through experimental validation, we demonstrate the suitability of our models for simulating real-depth cameras and air drag forces. Our depth camera noise model can replicate the values of a real depth camera sensor with a coefficient of determination (R2) value of 0.62, and our air drag force model improves accuracy by 51% compared to the Airsim simulation air drag force model in outdoor flying experiments at 10 m/s. |
| Databáze: | Directory of Open Access Journals |
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