62 citations · 137 across the 8 of their papers we have counts for
6 papers · 1 filter
All Eyes, no IMU: Learning Flight Attitude from Vision Alone
Jesse J. Hagenaars, Stein Stroobants, Sander M. Bohte +1
Vision is an essential part of attitude control for many flying animals, some of which have no dedicated sense of gravity. Flying robots, on the other hand, typically depend heavil…
On-Device Self-Supervised Learning of Low-Latency Monocular Depth from Only Events
Jesse Hagenaars, Yilun Wu, Federico Paredes-Vallés +2
Event cameras provide low-latency perception for only milliwatts of power. This makes them highly suitable for resource-restricted, agile robots such as small flying drones. Self-s…
Direct learning of home vector direction for insect-inspired robot navigation
Michiel Firlefyn, Jesse Hagenaars, Guido de Croon
Insects have long been recognized for their ability to navigate and return home using visual cues from their nest's environment. However, the precise mechanism underlying this rema…
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…
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…
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…