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Autonomous navigation in complex environments with a micro-aerial vehicle, Feb 6

In this talk, I will discuss approaches that enable a quadrotor to

autonomously navigate and explore complex indoor and outdoor

environments. Micro-aerial vehicles (MAVs), and in our case quadrotors,

offer exceptional 3D mobility over ground platforms, making them

particularly suitable for search-and-rescue missions in which the

vehicle must be able to navigate through complex 3D environments. In

such missions, especially in response to emergency or disaster

situations, it may not be safe for a human to enter the environment and

therefore the MAV must be able to operate fully autonomously without

requiring any human operator commands or external infrastructure. This

talk summarizes a sequence of projects that move towards the goal of

fully autonomous MAVs and will consist of three parts: (1) algorithms

and systems design that enable autonomous exploration of complex indoor

3D environments with a quadrotor equipped with a laser scanner, an IMU,

and limited computation; (2) a state estimation approach that permits

autonomous navigation in mixed indoor and outdoor environments using

laser, GPS and IMU; and (3) a vision-based state estimator that greatly

expand the capable operational environments of our quadrotor platform.

Extensive experimental evaluations are presented in each part of the



Shaojie Shen is a Ph.D candidate in the Department of Electrical and

Systems Engineering at the University of Pennsylvania. His research

interests include autonomous navigation of ground and aerial robots in

complex environments with focus on state estimation and mapping. His

work on autonomous micro-aerial vehicle has been covered by major media

outlets such as ABC, The New Yorker, and Discovery Channel.