# High Definition, Inexpensive, Underwater Mapping

arXiv: https://arxiv.org/abs/2203.05640 · HTML: https://mlsanity.com/paper/2203.05640/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 58 references that are in the map.
- First posted on arXiv 10 Mar 2022.

## Builds on (most cited first, 10 of 10)

- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017)
- [TEASER: Fast and Certifiable Point Cloud Registration](https://mlsanity.com/paper/2001.07715/) (2020)
- [The TUM VI Benchmark for Evaluating Visual-Inertial Odometry](https://mlsanity.com/paper/1804.06120/) (2018)
- [AQUALOC: An Underwater Dataset for Visual-Inertial-Pressure Localization](https://mlsanity.com/paper/1910.14532/) (2019)
- [SVIn2: An Underwater SLAM System using Sonar, Visual, Inertial, and Depth Sensor](https://mlsanity.com/paper/1810.03200/) (2018)
- [Experimental Comparison of Open Source Visual-Inertial-Based State Estimation Algorithms in the Underwater Domain](https://mlsanity.com/paper/1904.02215/) (2019)
- [Navigation in the Presence of Obstacles for an Agile Autonomous Underwater Vehicle](https://mlsanity.com/paper/1903.11750/) (2019)
- [AquaVis: A Perception-Aware Autonomous Navigation Framework for Underwater Vehicles](https://mlsanity.com/paper/2110.01646/) (2021)
- [Real-time Model-based Image Color Correction for Underwater Robots](https://mlsanity.com/paper/1904.06437/) (2019)

## Built on by (most cited first, 9 of 9)

- [The BabyView dataset: High-resolution egocentric videos of infants' and young children's everyday experiences](https://mlsanity.com/paper/2406.10447/) (2024)
- [Real-Time Dense 3D Mapping of Underwater Environments](https://mlsanity.com/paper/2304.02704/) (2023)
- [CaveSeg: Deep Semantic Segmentation and Scene Parsing for Autonomous Underwater Cave Exploration](https://mlsanity.com/paper/2309.11038/) (2023)
- [SM/VIO: Robust Underwater State Estimation Switching Between Model-based and Visual Inertial Odometry](https://mlsanity.com/paper/2304.01988/) (2023)
- [Semihierarchical Reconstruction and Weak-area Revisiting for Robotic Visual Seafloor Mapping](https://mlsanity.com/paper/2308.06147/) (2023)
- [Image-Based Relocalization and Alignment for Long-Term Monitoring of Dynamic Underwater Environments](https://mlsanity.com/paper/2503.04096/) (2025)
- [Enhancing Visual Inertial SLAM with Magnetic Measurements](https://mlsanity.com/paper/2409.09904/) (2024)
- [Long-Term Multi-Session 3D Reconstruction Under Substantial Appearance Change](https://mlsanity.com/paper/2602.20584/) (2026)
- [AquaFlow: A Monocular Gaussian Splatting SLAM for Underwater Streaming Reconstruction](https://mlsanity.com/paper/2608.22906/) (2026)

## Abstract (arXiv)

In this paper we present a complete framework for Underwater SLAM utilizing a single inexpensive sensor. Over the recent years, imaging technology of action cameras is producing stunning results even under the challenging conditions of the underwater domain. The GoPro 9 camera provides high definition video in synchronization with an Inertial Measurement Unit (IMU) data stream encoded in a single mp4 file. The visual inertial SLAM framework is augmented to adjust the map after each loop closure. Data collected at an artificial wreck of the coast of South Carolina and in caverns and caves in Florida demonstrate the robustness of the proposed approach in a variety of conditions.