Event-Based Angular Velocity Regression with Spiking Networks
release_ako4zieq4vg6fhf5cuq7w3psti
by
Mathias Gehrig, Sumit Bam Shrestha, Daniel Mouritzen, Davide
Scaramuzza
2020
Abstract
Spiking Neural Networks (SNNs) are bio-inspired networks that process
information conveyed as temporal spikes rather than numeric values. A spiking
neuron of an SNN only produces a spike whenever a significant number of spikes
occur within a short period of time. Due to their spike-based computational
model, SNNs can process output from event-based, asynchronous sensors without
any pre-processing at extremely lower power unlike standard artificial neural
networks. This is possible due to specialized neuromorphic hardware that
implements the highly-parallelizable concept of SNNs in silicon. Yet, SNNs have
not enjoyed the same rise of popularity as artificial neural networks. This not
only stems from the fact that their input format is rather unconventional but
also due to the challenges in training spiking networks. Despite their temporal
nature and recent algorithmic advances, they have been mostly evaluated on
classification problems. We propose, for the first time, a temporal regression
problem of numerical values given events from an event camera. We specifically
investigate the prediction of the 3-DOF angular velocity of a rotating event
camera with an SNN. The difficulty of this problem arises from the prediction
of angular velocities continuously in time directly from irregular,
asynchronous event-based input. Directly utilising the output of event cameras
without any pre-processing ensures that we inherit all the benefits that they
provide over conventional cameras. That is high-temporal resolution,
high-dynamic range and no motion blur. To assess the performance of SNNs on
this task, we introduce a synthetic event camera dataset generated from
real-world panoramic images and show that we can successfully train an SNN to
perform angular velocity regression.
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