# AQUALOC: An Underwater Dataset for Visual-Inertial-Pressure Localization

arXiv: https://arxiv.org/abs/1910.14532 · HTML: https://mlsanity.com/paper/1910.14532/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2019.

## Built on by (most cited first, 18 of 18)

- [Detector-Free Structure from Motion](https://mlsanity.com/paper/2306.15669/) (2023)
- [FLSea: Underwater Visual-Inertial and Stereo-Vision Forward-Looking Datasets](https://mlsanity.com/paper/2302.12772/) (2023)
- [VDN-NeRF: Resolving Shape-Radiance Ambiguity via View-Dependence Normalization](https://mlsanity.com/paper/2303.17968/) (2023)
- [High Definition, Inexpensive, Underwater Mapping](https://mlsanity.com/paper/2203.05640/) (2022)
- [Eiffel Tower: A Deep-Sea Underwater Dataset for Long-Term Visual Localization](https://mlsanity.com/paper/2305.05301/) (2023)
- [SUCRe: Leveraging Scene Structure for Underwater Color Restoration](https://mlsanity.com/paper/2212.09129/) (2022)
- [Learning Cross-Scale Visual Representations for Real-Time Image Geo-Localization](https://mlsanity.com/paper/2109.04087/) (2021)
- [Monocular visual simultaneous localization and mapping: (r)evolution from geometry to deep learning-based pipelines](https://mlsanity.com/paper/2503.02955/) (2025)
- [Image-Based Relocalization and Alignment for Long-Term Monitoring of Dynamic Underwater Environments](https://mlsanity.com/paper/2503.04096/) (2025)
- [Wavelet Dynamic Selection Network for Inertial Sensor Signal Enhancement](https://mlsanity.com/paper/2401.05416/) (2023)
- [Online Refractive Camera Model Calibration in Visual Inertial Odometry](https://mlsanity.com/paper/2409.12074/) (2024)
- [Knowledge Distillation for Underwater Feature Extraction and Matching via GAN-synthesized Images](https://mlsanity.com/paper/2504.08253/) (2025)
- [USIM and U0: A Vision-Language-Action Dataset and Model for General Underwater Robots](https://mlsanity.com/paper/2510.07869/) (2025)
- [Hyperspectral 3D Mapping of Underwater Environments](https://mlsanity.com/paper/2110.06571/) (2021)
- [6D Camera Relocalization in Visually Ambiguous Extreme Environments](https://mlsanity.com/paper/2207.06333/) (2022)
- [Deep Learning for Visual Navigation of Underwater Robots](https://mlsanity.com/paper/2310.19495/) (2023)
- [Attenuation-Aware Weighted Optical Flow with Medium Transmission Map for Learning-based Visual Odometry in Underwater terrain](https://mlsanity.com/paper/2407.13159/) (2024)
- [NSFlow: End-to-End Differentiable Neuro-Symbolic Optical Flow for Visual Odometry](https://mlsanity.com/paper/2609.06074/) (2026)

## Abstract (arXiv)

We present a new dataset, dedicated to the development of simultaneous localization and mapping methods for underwater vehicles navigating close to the seabed. The data sequences composing this dataset are recorded in three different environments: a harbor at a depth of a few meters, a first archaeological site at a depth of 270 meters and a second site at a depth of 380 meters. The data acquisition is performed using Remotely Operated Vehicles equipped with a monocular monochromatic camera, a low-cost inertial measurement unit, a pressure sensor and a computing unit, all embedded in a single enclosure. The sensors' measurements are recorded synchronously on the computing unit and seventeen sequences have been created from all the acquired data. These sequences are made available in the form of ROS bags and as raw data. For each sequence, a trajectory has also been computed offline using a Structure-from-Motion library in order to allow the comparison with real-time localization methods. With the release of this dataset, we wish to provide data difficult to acquire and to encourage the development of vision-based localization methods dedicated to the underwater environment. The dataset can be downloaded from: http://www.lirmm.fr/aqualoc/