activity
20152024
most citedLearning High-Speed Flight in the Wild

351 citations · 1.3k across the 34 of their papers we have counts for

collaborators
Showing 2018Show all

11 papers · 1 filter

cs.CV2018

Matching Features without Descriptors: Implicitly Matched Interest Points

Titus Cieslewski, Michael Bloesch, Davide Scaramuzza

The extraction and matching of interest points is a prerequisite for many geometric computer vision problems. Traditionally, matching has been achieved by assigning descriptors to…

cs.RO2018

Beauty and the Beast: Optimal Methods Meet Learning for Drone Racing

Elia Kaufmann, Mathias Gehrig, Philipp Foehn +4

Autonomous micro aerial vehicles still struggle with fast and agile maneuvers, dynamic environments, imperfect sensing, and state estimation drift. Autonomous drone racing brings t…

cs.CV2018

Asynchronous, Photometric Feature Tracking using Events and Frames

Daniel Gehrig, Henri Rebecq, Guillermo Gallego +1

We present a method that leverages the complementarity of event cameras and standard cameras to track visual features with low-latency. Event cameras are novel sensors that output…

cs.CV2018

Semi-Dense 3D Reconstruction with a Stereo Event Camera

Yi Zhou, Guillermo Gallego, Henri Rebecq +3

Event cameras are bio-inspired sensors that offer several advantages, such as low latency, high-speed and high dynamic range, to tackle challenging scenarios in computer vision. Th…

cs.RO2018

Deep Drone Racing: Learning Agile Flight in Dynamic Environments

Elia Kaufmann, Antonio Loquercio, Rene Ranftl +3

Autonomous agile flight brings up fundamental challenges in robotics, such as coping with unreliable state estimation, reacting optimally to dynamically changing environments, and…

cs.RO2018

A 64mW DNN-based Visual Navigation Engine for Autonomous Nano-Drones

Daniele Palossi, Antonio Loquercio, Francesco Conti +3

Fully-autonomous miniaturized robots (e.g., drones), with artificial intelligence (AI) based visual navigation capabilities are extremely challenging drivers of Internet-of-Things…