Title: Deep Learning for Semantic Segmentation on Minimal Hardware,
Abstract: Deep learning has revolutionised many fields, but it is still challenging to
transfer its success to small mobile robots with minimal hardware.
Specifically, some work has been done to this effect in the RoboCup humanoid
football domain, but results that are performant and efficient and still
generally applicable outside of this domain are lacking. We propose an approach
conceptually different from those taken previously. It is based on semantic
segmentation and does achieve these desired properties. In detail, it is being
able to process full VGA images in real-time on a low-power mobile processor.
It can further handle multiple image dimensions without retraining, it does not
require specific domain knowledge for achieving a high frame rate and it is
applicable on a minimal mobile hardware.