# AQUA-SLAM: Tightly-Coupled Underwater Acoustic-Visual-Inertial SLAM with Sensor Calibration

arXiv: https://arxiv.org/abs/2503.11420 · HTML: https://mlsanity.com/paper/2503.11420/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 42 references that are in the map.
- First posted on arXiv 14 Mar 2025.

## Builds on (most cited first, 9 of 9)

- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [Direct Sparse Odometry](https://mlsanity.com/paper/1607.02565/) (2016)
- [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017)
- [On-Manifold Preintegration for Real-Time Visual-Inertial Odometry](https://mlsanity.com/paper/1512.02363/) (2015)
- [Visual-Inertial Mapping with Non-Linear Factor Recovery](https://mlsanity.com/paper/1904.06504/) (2019)
- [SVIn2: An Underwater SLAM System using Sonar, Visual, Inertial, and Depth Sensor](https://mlsanity.com/paper/1810.03200/) (2018)
- [SM/VIO: Robust Underwater State Estimation Switching Between Model-based and Visual Inertial Odometry](https://mlsanity.com/paper/2304.01988/) (2023)
- [Tightly-coupled Visual-DVL-Inertial Odometry for Robot-based Ice-water Boundary Exploration](https://mlsanity.com/paper/2303.17005/) (2023)

## Built on by (most cited first, 2 of 2)

- [NAS-GS: Noise-Aware Sonar Gaussian Splatting](https://mlsanity.com/paper/2601.06285/) (2026)
- [ReefMapGS: Enabling Large-Scale Underwater Reconstruction by Closing the Loop Between Multimodal SLAM and Gaussian Splatting](https://mlsanity.com/paper/2604.11992/) (2026)

## Abstract (arXiv)

Underwater environments pose significant challenges for visual Simultaneous Localization and Mapping (SLAM) systems due to limited visibility, inadequate illumination, and sporadic loss of structural features in images. Addressing these challenges, this paper introduces a novel, tightly-coupled Acoustic-Visual-Inertial SLAM approach, termed AQUA-SLAM, to fuse a Doppler Velocity Log (DVL), a stereo camera, and an Inertial Measurement Unit (IMU) within a graph optimization framework. Moreover, we propose an efficient sensor calibration technique, encompassing multi-sensor extrinsic calibration (among the DVL, camera and IMU) and DVL transducer misalignment calibration, with a fast linear approximation procedure for real-time online execution. The proposed methods are extensively evaluated in a tank environment with ground truth, and validated for offshore applications in the North Sea. The results demonstrate that our method surpasses current state-of-the-art underwater and visual-inertial SLAM systems in terms of localization accuracy and robustness. The proposed system will be made open-source for the community.