Light deep learning models enriched with Entangled features for RGB-D semantic segmentation

Semantic segmentation is a crucial task in emerging robotic applications like autonomous driving and social robotics. State-of-the-art methods in this field rely on deep learning, with several works in the literature following the trend of using larger networks to achieve higher performance. However, this leads to greater model complexity and higher computational costs, which make it difficult to integrate such models on mobile robots. In this work we investigate how it is possible to obtain lighter performing deep models introducing additional data at a very low computational cost, instead of increasing the network complexity. We consider the features used in the 3D Entangled Forests algorithm, proposing different strategies to integrate such additional information into different deep networks. The new features allow to obtain lighter and performing segmentation models, either by shrinking the network size or improving existing networks proposed for real-time segmentation. Such result represents an interesting alternative in mobile robotics application, where computational power and energy are limited.