IEEE Kingston Section


Technical Talk: Fast Multirotor Flight Using Vision-Based Navigation in Real-World Environments 

The EMB/RA/CS Societies Joint Chapter of IEEE Kingston Section and Ingenuity Labs Research Institute are proud to present the following hybrid technical talk: 


Flying Flat Out: Fast Multirotor Flight Using Vision-Based Navigation in Real-World Environments 


Dr. Melissa Greeff

Department of Electrical and Computer Engineering

Queen’s University

Kingston, ON


1:30 – 2:30pm, Tuesday October 18, 2022

In-Person: Ingenuity Labs, Mitchell Hall (Room 395), Queen’s University


Meeting ID:  977 6073 7047, Passcode: Ingenuity


Multirotor unmanned aerial vehicles (UAVs) are mechanically simple and highly maneuverable robots that are suitable to a wide range of applications such as infrastructure inspection, transportation, search-and-rescue missions, and mapping operations. To support these applications, my research vision is to expand reliable autonomous multirotor navigation beyond lab demonstrations to real-world environments. To enable this, in the first part of this talk, we present computationally efficient control algorithms, by exploiting a property of the dynamics knowns as differential flatness.  We exploit this property to enable efficient prediction and safe learning using online data.  We develop safe high-performance control by accounting for nonlinear and unknown dynamics in a computationally tractable way. In the second part of this talk, we explore some of the challenges to high-speed autonomous vision-based flight. Real-world environments may be GPS-denied and vision-based navigation, relying predominantly on an onboard camera, is a lightweight and cost-effective alternative. Most standard controllers are perception-agnostic and tend to assume (i) the action computed by the controller has no effect on the ability of vision-based navigation to determine location and (ii) perfect state estimation is obtained. These assumptions often limit the reliability and performance of perception-agnostic controllers for autonomous vision-based flight. In the second part of this talk, we present perception-aware control algorithms that account for partial knowledge of the environment and plan despite imperfect state estimation. These approaches are validated through outdoor experiments on a DJI M600 multirotor where we demonstrate autonomous vision-based flight at speeds up to 10 m/s. Finally, I will discuss some of the open gaps to robust high-performance autonomous multirotor navigation.


Melissa Greeff joined Queens as an Assistant Professor in the department of Electrical and Computer Engineering and is a faculty affiliate with the Vector Institute for Artificial Intelligence. She obtained her BASc in Engineering Science and her PhD from the University of Toronto where she was a course instructor for Linear Algebra. Her research interests include aerial robots, vision-based navigation, and safe learning-based control. She has published in various international robotics and control systems venues including IEEE Robotics and Auto. Letters, Annual Review of Control, Robotics, and Autonomous Systems, ICRA, IROS and CDC. She has helped co-organize various workshops on safe robot learning and benchmarking at various international conferences.


Dr. Joshua Marshall       

Dr. Keyvan Hashtrudi-Zaad