Name
Abstract | ||
|---|---|---|
Underwater scenarios are challenging for visual Simultaneous Localization and Mapping (SLAM) due to limited visibility and intermittently losing structures in image views. In this paper, we propose a visual acoustic bundle adjustment system which fuses a camera and a Doppler Velocity Log (DVL) in a graph SLAM framework for reliable underwater localization and mapping. In order to fuse the vision with the acoustic measurements, an calibration algorithm is also designed to estimate extrinsic parameters between a camera and a DVL using features detected in scenes. Experimental results in a tank and an offshore wind farm show the proposed method can achieve better robustness and localization accuracy than pure visual SLAM, especially in visually challenging scenarios, and the extrinsic calibration parameters can be accurately estimated, even when initialized with a random guess. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IROS51168.2021.9636258 | 2021 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) |
DocType | ISSN | Citations |
Conference | 2153-0858 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shida Xu | 1 | 0 | 0.34 |
| Tomasz Luczynski | 2 | 0 | 0.34 |
| Jonatan Scharff Willners | 3 | 3 | 1.71 |
| Ziyang Hong | 4 | 0 | 0.68 |
| Kaicheng Zhang | 5 | 0 | 0.34 |
| Yvan Petillot | 6 | 142 | 19.16 |
| Sen Wang | 7 | 0 | 1.01 |