Title: Collision Selective Visual Neural Network Inspired by LGMD2 Neurons in Juvenile Locusts,
Abstract: For autonomous robots in dynamic environments mixed with human, it is vital
to detect impending collision quickly and robustly. The biological visual
systems evolved over millions of years may provide us efficient solutions for
collision detection in complex environments. In the cockpit of locusts, two
Lobula Giant Movement Detectors, i.e. LGMD1 and LGMD2, have been identified
which respond to looming objects rigorously with high firing rates. Compared to
LGMD1, LGMD2 matures early in the juvenile locusts with specific selectivity to
dark moving objects against bright background in depth while not responding to
light objects embedded in dark background - a similar situation which ground
vehicles and robots are facing with. However, little work has been done on
modeling LGMD2, let alone its potential in robotics and other vision-based
applications. In this article, we propose a novel way of modeling LGMD2 neuron,
with biased ON and OFF pathways splitting visual streams into parallel channels
encoding brightness increments and decrements separately to fulfill its
selectivity. Moreover, we apply a biophysical mechanism of spike frequency
adaptation to shape the looming selectivity in such a collision-detecting
neuron model. The proposed visual neural network has been tested with
systematic experiments, challenged against synthetic and real physical stimuli,
as well as image streams from the sensor of a miniature robot. The results
demonstrated this framework is able to detect looming dark objects embedded in
bright backgrounds selectively, which make it ideal for ground mobile
platforms. The robotic experiments also showed its robustness in collision
detection - it performed well for near range navigation in an arena with many
obstacles. Its enhanced collision selectivity to dark approaching objects
versus receding and translating ones has also been verified via systematic
experiments.