Maximum likelihood parameter identification for MAVs
Michael Burri, Janosch Nikolic, Helen Oleynikova, Markus W. Achtelik, Roland Siegwart
Abstract
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.
BibTeX
@inproceedings{icra2016_maximumlikelihoo,
title = {Maximum likelihood parameter identification for MAVs},
author = {Michael Burri and Janosch Nikolic and Helen Oleynikova and Markus W. Achtelik and Roland Siegwart},
booktitle = {ICRA 2016},
year = {2016}
}