We are interested in both fundamental aspects related to model-based state estimation and applications. Fundamental issues of interest include the propagation of uncertainty for 6 degrees-of-freedom rigid bodies, and the use of Kalman filtering for such systems.
We apply these ideas to localization using radio systems, and are interested in optimal estimation with low-cost sensors, in challenging environments, and where the system’s inherent instability means that we are reliant on accurate and reliable estimation.
Some relevant results include:
- Mark W. Mueller, Markus Hehn, Raffaello D’Andrea: Covariance correction step for Kalman filtering with an attitude, Journal of Guidance, Control, and Dynamics Article in advance, Nov. 2016. Publisher link.
Mueller, Mark W. A Dynamics-Agnostic State Estimator for Unmanned Aerial Vehicles Using Ultra-Wideband Radios. ASME 2018 Dynamic Systems and Control Conference, pp. V003T36A002-V003T36A002. American Society of Mechanical Engineers, 2018.
- Saman Fahandezh-Saadi, Mark W. Mueller: An algorithm for real-time restructuring of a ranging-based localization network, International Conference on Unmanned Aerial Systems (ICUAS), 2018.
- Saman Fahandezh-Saadi, Mark W. Mueller: Optimal measurement selection algorithm and estimator for ultra-wideband symmetric ranging localization, International Conference on Unmanned Aerial Systems (ICUAS), 2018.
- Mark W. Mueller, Michael Hamer, Raffaello D’Andrea: Fusing ultra-wideband range measurements with accelerometers and rate gyroscopes for quadrocopter state estimation, IEEE International Conference on Robotics and Automation (ICRA) 2015. Errata. Video.