D
Davide Falanga
Researcher at University of Zurich
Publications - 20
Citations - 1521
Davide Falanga is an academic researcher from University of Zurich. The author has contributed to research in topics: Drone & Odometry. The author has an hindex of 13, co-authored 20 publications receiving 907 citations.
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Journal ArticleDOI
The Foldable Drone: A Morphing Quadrotor That Can Squeeze and Fly
TL;DR: This work proposes a novel, simpler, yet effective morphing design for quadrotors consisting of a frame with four independently rotating arms that fold around the main frame and demonstrates the first work showing stable flight without requiring any symmetry of the morphology.
Journal ArticleDOI
Dynamic obstacle avoidance for quadrotors with event cameras
TL;DR: This work departed from state-of-the-art approaches by using event cameras, which are bioinspired sensors with reaction times of microseconds, and exploits the temporal information contained in the event stream to distinguish between static and dynamic objects and leverages a fast strategy to generate the motor commands necessary to avoid the approaching obstacles.
Proceedings ArticleDOI
Vision-based autonomous quadrotor landing on a moving platform
TL;DR: To the best of the knowledge, this is the first demonstration of a fully autonomous quadrotor system capable of landing on a moving target, using only on-board sensing and computing, without relying on any external infrastructure.
Proceedings ArticleDOI
Aggressive quadrotor flight through narrow gaps with onboard sensing and computing using active vision
TL;DR: In this paper, a trajectory that considers geometric, dynamic, and perception constraints is generated by fusing gap detection from a single onboard camera with an IMU to achieve autonomous, aggressive flight through narrow gaps using only onboard sensing and computing.
Proceedings ArticleDOI
PAMPC: Perception-Aware Model Predictive Control for Quadrotors
TL;DR: In this article, the authors present a perception-aware model predictive control framework for quadrotors that unifies control and planning with respect to action and perception objectives by leveraging numerical optimization to compute trajectories that satisfy system dynamics and require control inputs within the limits of the platform.