Maximum likelihood parameter identification for MAVs

As the applications of Micro Aerial Vehicles (MAVs) get more and more complex, and require highly dynamic motions, it becomes essential to have an accurate dynamic model of the MAV. Such a model can be used for reliable state estimation, control, and for realistic simulation. A good model requires accurate estimates of physical parameters of the system, which we aim to estimate from recorded flight data. In this paper, we present a detailed physical model of the MAV and a maximum likelihood estimation scheme for determining the dominant parameters, such as inertia matrix, center of gravity (CoG) with respect to the IMU, and parameters related to the aerodynamics. To incorporate all information given by the IMU and the physical MAV model, we propose to use two process models in the optimization. We show the effectiveness of the method on simulated data, as well as on a real platform.

Authors

• Michael Burri
• Janosch Nikolic
• Helen Oleynikova
• Markus W. Achtelik
• Roland Siegwart

Keywords

• Rotors
• Aerodynamics
• Solid modeling
• Vehicle dynamics
• Force
• Drag
• Maximum Likelihood
• Parameter Estimates
• Dynamic Model
• Process Model
• Simulated Data
• Maximum Likelihood Estimation
• Center Of Mass
• Aerodynamic
• Inertial Measurement Unit
• Inertia Matrix
• Flight Data
• Micro Air Vehicles
• White Noise
• Gaussian Noise
• Rotational Speed
• Time Instants
• Moment Of Inertia
• Process Noise
• State Trajectories
• Angular Speed
• White Noise Process
• Nominal Trajectory
• Accelerometer Measurements
• Total Moment
• Linear Least Squares Problem
• Unscented Kalman Filter
• Nominal Parameters
• Unit Quaternion
• Least Squares Problem
• Body Frame