VIA-SLAM: An Underwater Visual–Inertial–Acoustic SLAM With Integrated DVL
This paper proposes a Visual-Inertial-Acoustic SLAM (VIA-SLAM) for the localization of unmanned underwater vehicles (UUVs). The initialization of SLAM is a difficult task because of the slow-motion characteristics of UUVs and the challenging underwater environment with weak textures and low light conditions. The SLAM system integrates a Doppler Velocity Log (DVL), and we propose a DVL-assisted initialization method to enhance the accuracy of system initialization. The velocity measurements from the DVL are used to construct the DVL residuals which is jointly optimized with the reprojection residuals and the inertial residuals to enhance the localization accuracy of the system. In addition, the reliability of visual information is affected by the complex underwater environment. To address the problem of decreased localization accuracy due to poor image quality, we propose an adaptive dynamic weight factor to assess the impact of image quality on localization performance and accordingly adjust the weight of visual information. Finally, we evaluate the proposed VIA-SLAM using the open underwater datasets and the data collected from actual lake trials. The experimental results show that our SLAM system significantly improves localization accuracy compared with the state-of-the-art methods.
Authors
- Di Wang (ORCID: https://orcid.org/0000-0002-7732-1035)
- Junwei Wang (ORCID: https://orcid.org/0000-0002-9642-7724)
- Haoqian Huang (ORCID: https://orcid.org/0000-0001-9948-9624)
- Li Zhang (ORCID: https://orcid.org/0000-0001-6674-692X)
- Zhengqiong Liu (ORCID: https://orcid.org/0000-0003-1555-8923)
Institutions
- Hohai University (CN)
- Royal Holloway University of London (GB)
Publication Details
- Journal
- IEEE Transactions on Instrumentation and Measurement
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1109/tim.2025.3571157
- Citations
- 2
- Primary Topic
- Robotics and Sensor-Based Localization
- Type
- article
- Field-Weighted Citation Impact
- 9.99
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Jiangsu Province
- European Regional Development Fund