196 citations · 402 across the 11 of their papers we have counts for
11 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…
Lightweight Event-based Optical Flow Estimation via Iterative Deblurring
Yilun Wu, Federico Paredes-Vallés, Guido C. H. E. de Croon
Inspired by frame-based methods, state-of-the-art event-based optical flow networks rely on the explicit construction of correlation volumes, which are expensive to compute and sto…
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…
The Artificial Intelligence behind the winning entry to the 2019 AI Robotic Racing Competition
Christophe De Wagter, Federico Paredes-Vallés, Nilay Sheth +1
Robotics is the next frontier in the progress of Artificial Intelligence (AI), as the real world in which robots operate represents an enormous, complex, continuous state space wit…
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…
Neuromorphic control for optic-flow-based landings of MAVs using the Loihi processor
Julien Dupeyroux, Jesse Hagenaars, Federico Paredes-Vallés +1
Neuromorphic processors like Loihi offer a promising alternative to conventional computing modules for endowing constrained systems like micro air vehicles (MAVs) with robust, effi…
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…
How Do Neural Networks Estimate Optical Flow? A Neuropsychology-Inspired Study
D. B. de Jong, F. Paredes-Vallés, G. C. H. E. de Croon
End-to-end trained convolutional neural networks have led to a breakthrough in optical flow estimation. The most recent advances focus on improving the optical flow estimation by i…
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…
Unsupervised Learning of a Hierarchical Spiking Neural Network for Optical Flow Estimation: From Events to Global Motion Perception
Federico Paredes-Vallés, Kirk Y. W. Scheper, Guido C. H. E. de Croon
The combination of spiking neural networks and event-based vision sensors holds the potential of highly efficient and high-bandwidth optical flow estimation. This paper presents th…