# A Survey on Deep Learning Methods for Robot Vision

arXiv: https://arxiv.org/abs/1803.10862 · HTML: https://mlsanity.com/paper/1803.10862/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2018.

## Built on by (most cited first, 7 of 7)

- [The Challenges and Opportunities of Human-Centered AI for Trustworthy Robots and Autonomous Systems](https://mlsanity.com/paper/2105.04408/) (2021)
- [ComBiNet: Compact Convolutional Bayesian Neural Network for Image Segmentation](https://mlsanity.com/paper/2104.06957/) (2021)
- [Perceive, Represent, Generate: Translating Multimodal Information to Robotic Motion Trajectories](https://mlsanity.com/paper/2204.03051/) (2022)
- [Learning A Simulation-based Visual Policy for Real-world Peg In Unseen Holes](https://mlsanity.com/paper/2205.04297/) (2022)
- [The eyes and hearts of UAV pilots: observations of physiological responses in real-life scenarios](https://mlsanity.com/paper/2210.14910/) (2022)
- [iOSPointMapper: RealTime Pedestrian and Accessibility Mapping with Mobile AI](https://mlsanity.com/paper/2512.22392/) (2025)
- [Voronoi-based Second-order Descriptor with Whitened Metric in LiDAR Place Recognition](https://mlsanity.com/paper/2603.14974/) (2026)

## Abstract (arXiv)

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.