An Aerodynamic Model-aided State Estimator for Multi-rotor UAVs
Rongzhi Wang, Danping Zou, Changqing Xu , Ling Pei , Peilin Liu and Wenxian Yu
Shanghai Key Laboratory of Navigation and Location-based Services
Shanghai Jiao Tong University
A robust state estimator is presented by fusing the aerodynamic model of multi-rotor UAVs with measurements from optical flow and other low-cost sensors such as IMU, magnetometer, and ultrasonic sensor. Due to the particular aerodynamics of multi-rotor UAVs, the body velocity in the rotor plane is able to be measured by the accelerometer. We therefore propose a novel state estimator by fully exploring the characteristic of aerodynamics of multi-rotor UAV. Our state estimator is fast and easy to be implemented. We have tested our estimator with different platforms in different scenes. Experimental results show that our estimator performs robustly in low light conditions where existing methods usually fail.
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Note that in our previous research, we try to use machine learning method to predict the velocity from only the IMU data and found the approximated linear relationship between the velocity and the measured horizontal accelerations. But after we deeply invesitigate to the aerodyanmics about the multi-rotor UAVs, we find there is no need to treat it as a black box. Here is the previous work - "Velocity Prediction for Multi-rotor UAVs based on Maching Learning" .
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