Advancements in Sensor Fusion for Underwater SLAM: A Review on Enhanced Navigation and Environmental Perception
Underwater simultaneous localization and mapping (SLAM) has significant challenges due to the complexities of underwater environments, marked by limited visibility, variable conditions, and restricted global positioning system (GPS) availability. This study provides a comprehensive analysis of senso...
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MDPI AG
2024-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/23/7490 |
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| author | Fomekong Fomekong Rachel Merveille Baozhu Jia Zhizun Xu Bissih Fred |
| author_facet | Fomekong Fomekong Rachel Merveille Baozhu Jia Zhizun Xu Bissih Fred |
| author_sort | Fomekong Fomekong Rachel Merveille |
| collection | DOAJ |
| description | Underwater simultaneous localization and mapping (SLAM) has significant challenges due to the complexities of underwater environments, marked by limited visibility, variable conditions, and restricted global positioning system (GPS) availability. This study provides a comprehensive analysis of sensor fusion techniques in underwater SLAM, highlighting the amalgamation of proprioceptive and exteroceptive sensors to improve UUV navigational accuracy and system resilience. Essential sensor applications, including inertial measurement units (IMUs), Doppler velocity logs (DVLs), cameras, sonar, and LiDAR (light detection and ranging), are examined for their contributions to navigation and perception. Fusion methodologies, such as Kalman filters, particle filters, and graph-based SLAM, are evaluated for their benefits, limitations, and computational demands. Additionally, innovative technologies like quantum sensors and AI-driven filtering techniques are examined for their potential to enhance SLAM precision and adaptability. Case studies demonstrate practical applications, analyzing the compromises between accuracy, computational requirements, and adaptability to environmental changes. This paper proceeds to emphasize future directions, stressing the need for advanced filtering and machine learning to address sensor drift, noise, and environmental unpredictability, hence improving autonomous underwater navigation through reliable sensor fusion. |
| format | Article |
| id | doaj-art-2c46dca702d342aea6fa55313268cecd |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-2c46dca702d342aea6fa55313268cecd2025-08-20T01:55:33ZengMDPI AGSensors1424-82202024-11-012423749010.3390/s24237490Advancements in Sensor Fusion for Underwater SLAM: A Review on Enhanced Navigation and Environmental PerceptionFomekong Fomekong Rachel Merveille0Baozhu Jia1Zhizun Xu2Bissih Fred3School of Naval Architecture and Maritime, Guangdong Ocean University, Zhanjiang 524000, ChinaSchool of Naval Architecture and Maritime, Guangdong Ocean University, Zhanjiang 524000, ChinaSchool of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UKCollege of Fisheries, Guangdong Ocean University, Zhanjiang 524088, ChinaUnderwater simultaneous localization and mapping (SLAM) has significant challenges due to the complexities of underwater environments, marked by limited visibility, variable conditions, and restricted global positioning system (GPS) availability. This study provides a comprehensive analysis of sensor fusion techniques in underwater SLAM, highlighting the amalgamation of proprioceptive and exteroceptive sensors to improve UUV navigational accuracy and system resilience. Essential sensor applications, including inertial measurement units (IMUs), Doppler velocity logs (DVLs), cameras, sonar, and LiDAR (light detection and ranging), are examined for their contributions to navigation and perception. Fusion methodologies, such as Kalman filters, particle filters, and graph-based SLAM, are evaluated for their benefits, limitations, and computational demands. Additionally, innovative technologies like quantum sensors and AI-driven filtering techniques are examined for their potential to enhance SLAM precision and adaptability. Case studies demonstrate practical applications, analyzing the compromises between accuracy, computational requirements, and adaptability to environmental changes. This paper proceeds to emphasize future directions, stressing the need for advanced filtering and machine learning to address sensor drift, noise, and environmental unpredictability, hence improving autonomous underwater navigation through reliable sensor fusion.https://www.mdpi.com/1424-8220/24/23/7490underwater simultaneous localization and mapping (SLAM)sensor fusionunmanned underwater vehicles (UUVs)Kalman filterparticle filtergraph-based SLAM |
| spellingShingle | Fomekong Fomekong Rachel Merveille Baozhu Jia Zhizun Xu Bissih Fred Advancements in Sensor Fusion for Underwater SLAM: A Review on Enhanced Navigation and Environmental Perception Sensors underwater simultaneous localization and mapping (SLAM) sensor fusion unmanned underwater vehicles (UUVs) Kalman filter particle filter graph-based SLAM |
| title | Advancements in Sensor Fusion for Underwater SLAM: A Review on Enhanced Navigation and Environmental Perception |
| title_full | Advancements in Sensor Fusion for Underwater SLAM: A Review on Enhanced Navigation and Environmental Perception |
| title_fullStr | Advancements in Sensor Fusion for Underwater SLAM: A Review on Enhanced Navigation and Environmental Perception |
| title_full_unstemmed | Advancements in Sensor Fusion for Underwater SLAM: A Review on Enhanced Navigation and Environmental Perception |
| title_short | Advancements in Sensor Fusion for Underwater SLAM: A Review on Enhanced Navigation and Environmental Perception |
| title_sort | advancements in sensor fusion for underwater slam a review on enhanced navigation and environmental perception |
| topic | underwater simultaneous localization and mapping (SLAM) sensor fusion unmanned underwater vehicles (UUVs) Kalman filter particle filter graph-based SLAM |
| url | https://www.mdpi.com/1424-8220/24/23/7490 |
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