2016 IEEE International Conference on Robotics and Automation (ICRA) 2016
DOI: 10.1109/icra.2016.7487627
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Maximum likelihood parameter identification for MAVs

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 f… Show more

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Cited by 22 publications
(21 citation statements)
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“…The scalar k drag lumps together the aerodynamic drag force of the body and the aerodynamic forces due to the blade flapping. The MAV model parameters can be estimated using the method proposed by Burri et al (2016).…”
Section: Force Estimation Techniquesmentioning
confidence: 99%
“…The scalar k drag lumps together the aerodynamic drag force of the body and the aerodynamic forces due to the blade flapping. The MAV model parameters can be estimated using the method proposed by Burri et al (2016).…”
Section: Force Estimation Techniquesmentioning
confidence: 99%
“…The importance of these effects stems from the fact that they introduce additional forces in the x − y rotor plane, adding some damping to the MAV velocity as shown in [15]. It is possible to combine these effects as shown in [16], [17] into one lumped drag coefficient k D .…”
Section: Modelmentioning
confidence: 99%
“…The data is subscribed by the Multi-Sensor Fusion (MSF) framework [8] to filter noisy measurement and to compensate for possible delay in the WiFi network connection. The ground station sets either a goal position as denoted [p * , q * ] for position and orientation or N sequences, [p * 1:N , q * 1:N ], generated by the trajectory generator [17].…”
Section: B Software Setupmentioning
confidence: 99%