High-Fidelity Drone Simulation with Depth Camera Noise and Improved Air Drag Force Models

Abstract

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.

Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems

“…It has an RGB frame rate of 30 FPS and an RGB frame resolution of 1920 x 1080. Intel Realsense D435 was used in reference [272] to collect depth measurements in order to identify the parameters of the depth camera noise model, thus enabling more accurate SLAM algorithm execution. RGB-D cameras can also be involved in 3D mapping to describe a UAV's surroundings after SLAM fusion with a UAV [273].…”

Non-Local Means Hole Repair Algorithm Based on Adaptive Block

“…In recent years, depth cameras have been widely utilized in various fields, such as autonomous driving [1][2][3], robot grasping [4][5][6], and simultaneous localization and mapping (SLAM) [7][8][9]. However, due to object material, specular reflection, or occlusion, the captured depth data often exhibits holes at the edges where objects come into contact with the background.…”