62 citations · 63 across the 2 of their papers we have counts for
7 papers
Fully neuromorphic vision and control for autonomous drone flight
Federico Paredes-Vallés, Jesse Hagenaars, Julien Dupeyroux +3
Biological sensing and processing is asynchronous and sparse, leading to low-latency and energy-efficient perception and action. In robotics, neuromorphic hardware for event-based…
Taming Contrast Maximization for Learning Sequential, Low-latency, Event-based Optical Flow
Federico Paredes-Vallés, Kirk Y. W. Scheper, Christophe De Wagter +1
Event cameras have recently gained significant traction since they open up new avenues for low-latency and low-power solutions to complex computer vision problems. To unlock these…
NanoFlowNet: Real-time Dense Optical Flow on a Nano Quadcopter
Rik J. Bouwmeester, Federico Paredes-Vallés, Guido C. H. E. de Croon
Nano quadcopters are small, agile, and cheap platforms that are well suited for deployment in narrow, cluttered environments. Due to their limited payload, these vehicles are highl…
Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networks
Jesse Hagenaars, Federico Paredes-Vallés, Guido de Croon
The field of neuromorphic computing promises extremely low-power and low-latency sensing and processing. Challenges in transferring learning algorithms from traditional artificial…
Back to Event Basics: Self-Supervised Learning of Image Reconstruction for Event Cameras via Photometric Constancy
F. Paredes-Vallés, G. C. H. E. de Croon
Event cameras are novel vision sensors that sample, in an asynchronous fashion, brightness increments with low latency and high temporal resolution. The resulting streams of events…
Evolved Neuromorphic Control for High Speed Divergence-based Landings of MAVs
J. J. Hagenaars, F. Paredes-Vallés, S. M. Bohté +1
Flying insects are capable of vision-based navigation in cluttered environments, reliably avoiding obstacles through fast and agile maneuvers, while being very efficient in the pro…