ReviewSensors (Basel, Switzerland)2024
Advancements in Sensor Fusion for Underwater SLAM: A Review on Enhanced Navigation and Environmental Perception.
Review in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Underwater detection and repair reinforcement technologies for reservoir-dam systems: development status and research challenges.Communications engineering · 2026Review
- Multisensor Localization and Risk-Aware Navigation for Underwater Robots in Turbid and Confined Inland Waters: A Review.Sensors (Basel, Switzerland) · 2026Review
- Research on Navigation Method for Subsea Drilling Robot Based on Inertial Navigation and Odometry.Sensors (Basel, Switzerland) · 2026Article
- VQF-Based Decoupled Navigation Architecture for High-Curvature Maneuvering of Underwater Vehicles.Sensors (Basel, Switzerland) · 2026Article
- A Novel Structural Vibration Sensing Approach Based on a Miniaturized Inertial Measurement Unit.Sensors (Basel, Switzerland) · 2025Article
- Deep Reinforcement Learning of Mobile Robot Navigation in Dynamic Environment: A Review.Sensors (Basel, Switzerland) · 2025Review
- Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions.Sensors (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.