1 research outputs found
Integrating sensor models in deep learning boosts performance: application to monocular depth estimation in warehouse automation
Deep learning is the mainstream paradigm in computer vision and machine learning,
but performance is usually not as good as expected when used for applications in robot vision.
The problem is that robot sensing is inherently active, and often, relevant data is scarce for many
application domains. This calls for novel deep learning approaches that can offer a good performance
at a lower data consumption cost. We address here monocular depth estimation in warehouse
automation with new methods and three different deep architectures. Our results suggest that the
incorporation of sensor models and prior knowledge relative to robotic active vision, can consistently
improve the results and learning performance from fewer than usual training samples, as compared
to standard data-driven deep learning