# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 9 Research Gaps and Opportunities
### Gaps in Underwater SLAM Research
#### Cost and Power Consumption of Sensors  
Underwater SLAM faces significant challenges due to the complex underwater environment, characterized by limited visibility, variable conditions, and restricted GNSS availability. This puts high demands on exteroceptive sensors (sonars, cameras, and DVLs) as well as on proprioceptive sensors (IMU), to enhance the navigational accuracy and resilience of unmanned underwater vehicles. This becomes a major concern, particularly for long-duration missions.  
A study by Merveille et al. analyzes sensor fusion techniques, combining proprioceptive and exteroceptive sensors to enhance UUV navigation [170]. They explore the use of sensors, including IMUs, DVLs, cameras, sonar, and LiDAR, and further enhance their capabilities through various filtering methods. The results are compared to computationally efficient emerging technologies such as quantum sensors and AI-driven filtering. The power consumption for such systems presents itself as a bottleneck for wider applications. The study highlights the trade-offs between accuracy, computational and power demands, and adaptability according to environmental changes.  
Another related limitation in the use of underwater sensors for navigation and SLAM is the high cost of sensors operating in difficult environments. Acoustic modems and positioning systems have been designed for military and oil and gas industries, requiring deep water deployments and high reliability, making them expensive and unsuitable for low-cost applications. However, recent advancements in low-cost unmanned vehicles like ROVs and AUVs for shallow water missions and the need for sensor networks to monitor water quality and climate change effects have driven the development of low-cost, low-power acoustic modems and positioning systems.  
In a study on navigation techniques for inspection and data acquisition in UWSNs, Wibisono et al. discussed implementing a dynamic homing control algorithm in AUVs, which helps enhance data acquisition by directing movement based on the importance of information at each point. This approach not only improves observation accuracy but also optimizes time and power usage, ensuring the collected data are of maximum value for mission or research objectives with the least power required for sensors and AUV navigation.  
A study by Campagnaro et al. found that low-cost sensors with reduced transmission and positioning range and precision are actually suitable for shallow water environments where obstacles limit long-range transmissions [171]. The paper further reviews recent developments in low-cost acoustic communication and positioning systems, analyzing university prototypes and commercial devices, and exploring potential new applications. The study concludes by urging developers to focus on the growing demand for low-cost sensors, especially for swarm applications, and calls on the research community to provide easy-to-understand and implement manuals for these sensors.  
SVIn2 is a novel SLAM system designed by Rahman et al. for challenging underwater environments, focusing on cost savings by reducing the number of necessary sensors [74]. Unlike previous systems that require DVL or expensive INS, SVIn2 uses a unique sensor configuration where the mechanical scanning sonar maps the vertical plane parallel to the image plane, enabling the mapping of cave structures. It integrates scanning profiling sonar, visual, inertial, and water-pressure data in a non-linear optimization framework. The open-source software has been validated in benchmark datasets and real-world scenarios, showing excellent accuracy and robustness.