A Visual-Inertial Pressure Fusion-Based Underwater Simultaneous Localization and Mapping System

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. B...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2024-05, Vol.24 (10), p.3207
Main Authors: Lu, Zhufei, Xu, Xing, Luo, Yihao, Ding, Lianghui, Zhou, Chao, Wang, Jiarong
Format: Article
Language:English
Subjects:
Algorithms
Cameras
Localization
Mines, Submarine
multi-sensor fusion
Normal distribution
Optimization
ORB-SLAM
Random variables
Remote submersibles
Sensors
Sonar
Sonar systems
underwater SLAM
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Summary: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 -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%.
ISSN:1424-8220
1424-8220
DOI:10.3390/s24103207