A Survey on Deep Learning Methods for Robot Vision

TL;DR

This survey reviews how deep learning techniques are applied to robot vision, covering models, methodologies, key research works, and future trends to guide developers in the field.

Contribution

It provides a comprehensive overview of deep learning in robot vision, including models, methodologies, and a review of principal research and future directions.

Findings

Deep learning has significantly advanced robot vision capabilities.

Various neural models are effectively used in robot vision applications.

The survey highlights current trends and future challenges in the field.

Abstract

Deep learning has allowed a paradigm shift in pattern recognition, from using hand-crafted features together with statistical classifiers to using general-purpose learning procedures for learning data-driven representations, features, and classifiers together. The application of this new paradigm has been particularly successful in computer vision, in which the development of deep learning methods for vision applications has become a hot research topic. Given that deep learning has already attracted the attention of the robot vision community, the main purpose of this survey is to address the use of deep learning in robot vision. To achieve this, a comprehensive overview of deep learning and its usage in computer vision is given, that includes a description of the most frequently used neural models and their main application areas. Then, the standard methodology and tools used for designing deep-learning based vision systems are presented. Afterwards, a review of the principal work using deep learning in robot vision is presented, as well as current and future trends related to the use of deep learning in robotics. This survey is intended to be a guide for the developers of robot vision systems.