This paper proposes a tightly-coupled visual-Doppler-Velocity-Log (visual-DVL) fusion method for underwater robot localization through integrating the velocity measurements from a DVL into a visual odometry (VO). Considering that employing the DVL measurements in dead-reckoning systems easily leads to error accumulation and suboptimal results in previous works, we directly integrate them into the visual tracking process. Specifically, the velocity measurements are utilized to improve the initial estimation of camera pose during visual tracking, aiming to provide a better initial value for pose optimization. Thereafter, these velocity measurements are also directly employed to constrain the position change of the camera between two adjacent frames by constructing a novel DVL error term, which is optimized jointly with the visual constrains to obtain a more accurate camera pose. Various experiments are carried out in the datasets collected from several scenarios of the underwater simulation environment HoloOcean, and the results illustrate that the proposed fusion method can effectively improve the localization accuracy for underwater robots by about 20% compared to pure visual odometry. The proposed method provides valuable guidance for the accurate localization of underwater robots.
Paper
Full text
Tightly-Coupled Visual-DVL Fusion For Accurate Localization of Underwater Robots
Semantic Scholar · Engineering · 2023
Abstract
This paper proposes a tightly-coupled visual-Doppler-Velocity-Log (visual-DVL) fusion method for underwater robot localization through integrating the velocity measurements from a DVL into a visual odometry (VO). Considering that employing the DVL measurements in dead-reckoning systems easily leads to error accumulation and suboptimal results in previous works, we directly integrate them into the visual tracking process. Specifically, the velocity measurements are utilized to improve the initial estimation of camera pose during visual tracking, aiming to provide a better initial value for pose optimization. Thereafter, these velocity measurements are also directly employed to constrain the position change of the camera between two adjacent frames by constructing a novel DVL error term, which is optimized jointly with the visual constrains to obtain a more accurate camera pose. Various experiments are carried out in the datasets collected from several scenarios of the underwater simulation environment HoloOcean, and the results illustrate that the proposed fusion method can effectively improve the localization accuracy for underwater robots by about 20% compared to pure visual odometry. The proposed method provides valuable guidance for the accurate localization of underwater robots.