Date of Award


Degree Type


Degree Name

Doctor of Philosophy (PhD)

Graduate Group

Electrical & Systems Engineering

First Advisor

Vijay Kumar


Quadrotors and multirotors in general are common in many inspection and surveillance

applications. For these applications, visual-inertial odometry is a common way to localize

the vehicles and observe the environment. However, unlike with wheeled mobile robots,

quadrotor localization algorithms often do not use knowledge of the control inputs and

the full vehicle dynamics as a process model for localization. Rather, they use kinematic

models, with the IMU providing acceleration and angular velocity. One of the reasons for

avoiding the use of dynamics is that, until recently quadrotor aerodynamic effects have not

been considered in the literature and hence the dynamic models for quadrotors have been

less accurate than those for wheeled mobile robots. The main aerodynamic terms that are

significant are first-order effects that are linear in velocity and angular velocity. They are

predominantly caused by aerodynamic interaction with the spinning propellers. This work

investigates the models for such effects, as well as what can be gained if such aerodynamic

effects are incorporated into the dynamic model and the full dynamics are used for state

estimation. We develop novel IMU-based filters, the end results of which are used to estimate

the wind velocity of the quadrotor or, indoors, when the ambient wind is zero, the velocity

of the quadrotor. In addition, these filters estimate the many aerodynamic parameters in

the model online. They may also be used to estimate sensor biases and inertial parameters.

We demonstrate the effectiveness of these filters through experiments. We also present

nonlinear observability analyses that theoretically determine the observability properties of

the systems.

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Robotics Commons