Efficient Visual Perception of Human-Robot Walking Environments Using Semi-Supervised Learning
Dmytro Kuzmenko,Oleksii Tsepa,Andrew Garrett Kurbis,Alex Mihailidis,Brokoslaw Laschowski,Dmytro Kuzmenko,Oleksii Tsepa,Andrew Garrett Kurbis,Alex Mihailidis,Brokoslaw Laschowski
Convolutional neural networks trained using supervised learning can improve visual perception for human-robot walking. These advances have been possible due to largescale datasets like ExoNet and StairNet - the largest open-source image datasets of real-world walking environments. However, these datasets require vast amounts of manually annotated data, the development of which is time consuming an...
