A Visual–Inertial Pressure Fusion-Based Underwater Simultaneous Localization and Mapping System
Author:
Lu Zhufei12, Xu Xing3, Luo Yihao1ORCID, Ding Lianghui3, Zhou Chao4, Wang Jiarong4
Affiliation:
1. Yichang Testing Technique R&D Institute, Yichang 443003, China 2. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China 3. Institute of Image Communication and Network Engineering, Shanghai Jiaotong University, Shanghai 200240, China 4. Unit 92228 of the Chinese People’s Liberation Army, Beijing 100072, China
Abstract
Detecting objects, particularly naval mines, on the seafloor is a complex task. In naval mine countermeasures (MCM) operations, sidescan or synthetic aperture sonars have been used to search large areas. However, a single sensor cannot meet the requirements of high-precision autonomous navigation. Based on the ORB-SLAM3-VI framework, we propose ORB-SLAM3-VIP, which integrates a depth sensor, an IMU sensor and an optical sensor. This method integrates the measurements of depth sensors and an IMU sensor into the visual SLAM algorithm through tight coupling, and establishes a multi-sensor fusion SLAM model. Depth constraints are introduced into the process of initialization, scale fine-tuning, tracking and mapping to constrain the position of the sensor in the z-axis and improve the accuracy of pose estimation and map scale estimate. The test on seven sets of underwater multi-sensor sequence data in the AQUALOC dataset shows that, compared with ORB-SLAM3-VI, the ORB-SLAM3-VIP system proposed in this paper reduces the scale error in all sequences by up to 41.2%, and reduces the trajectory error by up to 41.2%. The square root has also been reduced by up to 41.6%.
Reference17 articles.
1. Xie, Y., Bore, N., and Folkesson, J. (2022). Sidescan Only Neural Bathymetry from Large-Scale Survey. Sensors, 22. 2. Ribas, D., Ridao, P., Neira, J., and Tardos, J.D. (2006, January 9–15). SLAM using an Imaging Sonar for Partially Structured Underwater Environments. Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, China. 3. Mallios, A., Ridao, P., Ribas, D., Maurelli, F., and Petillot, Y. (2010, January 18–22). EKF-SLAM for AUV navigation under probabilistic sonar scan-matching. Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan. 4. Rahman, S., Li, A.Q., and Rekleitis, I. (2018). SVIn2: Sonar Visual-Inertial SLAM with Loop Closure for Underwater Navigation. arXiv. 5. Lynen, S., Achtelik, M.W., Weiss, S., Chli, M., and Siegwart, R. (2013, January 3–7). A robust and modular multi- sensor fusion approach applied to mav navigation. Proceedings of the 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, Tokyo, Japan.
|
|