DogSurf: Quadruped Robot Capable of GRU-based Surface Recognition for
  Blind Person Navigation
Artem Bazhenov Vladimir Berman Sergei Satsevich Olga Shalopanova Miguel Altamirano Cabrera Artem Lykov Dzmitry Tsetserukou
This paper introduces DogSurf - a newapproach of using quadruped robots to
help visually impaired people navigate in real world. The presented method
allows the quadruped robot to detect slippery surfaces, and to use audio and
haptic feedback to inform the user when to stop. A state-of-the-art GRU-based
neural network architecture with mean accuracy of 99.925% was proposed for the
task of multiclass surface classification for quadruped robots. A dataset was
collected on a Unitree Go1 Edu robot. The dataset and code have been posted to
the public domain.