# Tightly-coupled Visual-DVL-Inertial Odometry for Robot-based Ice-water Boundary Exploration

arXiv: https://arxiv.org/abs/2303.17005 · HTML: https://mlsanity.com/paper/2303.17005/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 66 references that are in the map.
- First posted on arXiv 29 Mar 2023.

## 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)
- [LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping](https://mlsanity.com/paper/2104.10831/) (2021)
- [Visual-Inertial Navigation: A Concise Review](https://mlsanity.com/paper/1906.02650/) (2019)
- [Robust Stereo Visual Inertial Odometry for Fast Autonomous Flight](https://mlsanity.com/paper/1712.00036/) (2017)
- [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)
- [Real-time Monocular Visual Odometry for Turbid and Dynamic Underwater Environments](https://mlsanity.com/paper/1806.05842/) (2018)
- [LiDAR Enhanced Structure-from-Motion](https://mlsanity.com/paper/1911.03369/) (2019)
- [Adaptive Navigation Scheme for Optimal Deep-Sea Localization Using Multimodal Perception Cues](https://mlsanity.com/paper/1906.04888/) (2019)

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

- [OceanSim: A GPU-Accelerated Underwater Robot Perception Simulation Framework](https://mlsanity.com/paper/2503.01074/) (2025)
- [AQUA-SLAM: Tightly-Coupled Underwater Acoustic-Visual-Inertial SLAM with Sensor Calibration](https://mlsanity.com/paper/2503.11420/) (2025)

## Abstract (arXiv)

Robotic underwater systems, e.g., Autonomous Underwater Vehicles (AUVs) and Remotely Operated Vehicles (ROVs), are promising tools for collecting biogeochemical data at the ice-water interface for scientific advancements. However, state estimation, i.e., localization, is a well-known problem for robotic systems, especially, for the ones that travel underwater. In this paper, we present a tightly-coupled multi-sensors fusion framework to increase localization accuracy that is robust to sensor failure. Visual images, Doppler Velocity Log (DVL), Inertial Measurement Unit (IMU) and Pressure sensor are integrated into the state-of-art Multi-State Constraint Kalman Filter (MSCKF) for state estimation. Besides that a new keyframe-based state clone mechanism and a new DVL-aided feature enhancement are presented to further improve the localization performance. The proposed method is validated with a data set collected in the field under frozen ice, and the result is compared with 6 other different sensor fusion setups. Overall, the result with the keyframe enabled and DVL-aided feature enhancement yields the best performance with a Root-mean-square error of less than 2 m compared to the ground truth path with a total traveling distance of about 200 m.