UniVIO: Unified Direct and Feature-Based Underwater Stereo Visual-Inertial Odometry

• Published in: IEEE transactions on instrumentation and measurement Vol. 71; pp. 1 - 14
• Main Authors: Miao, Ruihang, Qian, Jiuchao, Song, Yang, Ying, Rendong, Liu, Peilin
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Cameras; Data models; Datasets; Distortion; Errors; Forecasting; Image reconstruction; Location awareness; Optical flow (image analysis); Optimization; Real-time systems; Robustness; Trajectory; Underwater; Underwater dataset; underwater image rectification; unified sparse model; Visual odometry; visual-inertial odometry (VIO)
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2021.3136259

Providing an accurate trajectory in an underwater scene is challenging for visual odometry due to weak texture and varying illumination. In this article, we propose a novel underwater visual-inertial odometry (VIO) with the unifying direct method and feature-based method. Our approach starts with a new rectification method for underwater images. The new underwater image rectification method separately eliminates water-air refraction distortion and lens distortion with an approximate single viewpoint (SVP) camera model. Meanwhile, a unified optimization method is used in our approach to obtain robust and highly accurate odometry results. This method jointly optimizes projection errors and photometric errors. Moreover, the robust data association processing, which combines keyframe / keypoint selection, keypoint position prediction, and robust local optical flow, was designed in our approach. The robust data association processing greatly improves the success rate of keypoints tracking. To evaluate the precision of underwater visual odometry, an underwater dataset with complete and highly accurate ground truth trajectories is provided in this work. The performance of our system is compared with other state-of-the-art algorithms on multiple datasets, including visual odometry datasets and our proposed underwater dataset. The evaluation results show that the proposed system can achieve better results than the state-of-the-art algorithms. Furthermore, the proposed system is validated on public natural underwater datasets to show the performance of the system in a real underwater environment. The qualitative results demonstrate that the proposed system can work well in the natural underwater environment.