# Deep Learning for Visual Navigation of Underwater Robots

arXiv: https://arxiv.org/abs/2310.19495 · HTML: https://mlsanity.com/paper/2310.19495/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 30 Oct 2023.

## Builds on (most cited first, 7 of 7)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Fast Underwater Image Enhancement for Improved Visual Perception](https://mlsanity.com/paper/1903.09766/) (2019)
- [Enhancing Underwater Imagery using Generative Adversarial Networks](https://mlsanity.com/paper/1801.04011/) (2018)
- [AQUALOC: An Underwater Dataset for Visual-Inertial-Pressure Localization](https://mlsanity.com/paper/1910.14532/) (2019)
- [Vision-Based Goal-Conditioned Policies for Underwater Navigation in the Presence of Obstacles](https://mlsanity.com/paper/2006.16235/) (2020)
- [A Deep Learning Approach To Dead-Reckoning Navigation For Autonomous Underwater Vehicles With Limited Sensor Payloads](https://mlsanity.com/paper/2110.00661/) (2021)
- [Virtual Underwater Datasets for Autonomous Inspections](https://mlsanity.com/paper/2209.06013/) (2022)

## Abstract (arXiv)

This paper aims to briefly survey deep learning methods for visual navigation of underwater robotics. The scope of this paper includes the visual perception of underwater robotics with deep learning methods, the available visual underwater datasets, imitation learning, and reinforcement learning methods for navigation. Additionally, relevant works will be categorized under the imitation learning or deep learning paradigm for underwater robots for clarity of the training methodologies in the current landscape. Literature that uses deep learning algorithms to process non-visual data for underwater navigation will not be considered, except as contrasting examples.