M2APix: a bio-inspired auto-adaptive visual sensor for robust ground height estimation


This paper presents for the first time the embedded stand-alone version of the bio-inspired M2APix (Michaelis-Menten auto-adaptive pixels) sensor as a ventral optic flow sensor to endow glider-type unmanned aerial vehicles with autonomous landing ability. Assuming the aircraft is equipped with any reliable speed measurement system such as a global positioning system or an inertial measurement unit, we can use the velocity of the glider to determine with high precision its height while landing. This information is robust to different outdoor lighting conditions and over different kinds of textured ground, a crucial property to control the landing phase of the aircraft.

ISCAS2018, IEEE International Symposium on Circuits and Systems
Victor Boutin
Victor Boutin
Phd in Computational Neuroscience

During my PhD, I focused on predictive coding in bio-inspired neural networks.

Laurent U Perrinet
Laurent U Perrinet
Researcher in Computational Neuroscience

My research interests include Machine Learning and computational neuroscience applied to Vision.