# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 5 Sensors Driving Underwater SLAM  
In the previous section, we classified underwater SLAM methods based on their underlying principles, sensors, and computational approaches. Building on that foundation, we now focus on the sensing technologies that form the backbone of these SLAM systems. Underwater environments pose unique challenges, including the absence of GNSS signals, variable visibility conditions, and the presence of dynamic elements such as marine life or moving particles. Many sensors and modalities have been employed to overcome these obstacles. These range from acoustic sensors that leverage the propagation characteristics of sound waves underwater to optical sensors that capture high-resolution imagery in more apparent conditions to emerging laser-based and multi-sensor fusion approaches. Understanding these sensing technologies' capabilities, limitations, and suitable applications is critical for developing robust and accurate underwater SLAM solutions.  
This section delves into the primary sensing modalities and their roles in underwater SLAM. We begin by examining vision-based sensors, which provide rich visual information essential for detailed mapping but whose performance is often constrained by water turbidity and lighting conditions. We then discuss acoustic sensors, such as sonar, that offer long-range coverage and reliability in low-visibility settings, albeit at lower resolution. We also consider emerging technologies like laser-based sensors that combine high accuracy with underwater-appropriate propagation characteristics. Finally, we explore sensor fusion techniques, which integrate data from multiple sensor types to enhance robustness, improve map quality, and provide a complete understanding of the environment. Figure 9 summarizes the sensors and technologies utilized in each stage of the SLAM algorithm.  
Figure 9: Overview of sensors and methods used in underwater SLAM applications. The figure presents a comprehensive summary of sensors, front-end VO methods, loop closure detection techniques, and back-end state estimation algorithms. This visualization highlights various combinations of sensing technologies and SLAM methodologies that address the challenges of underwater navigation. (Adapted from Zhang et al. [27]).  
By analyzing these sensing modalities' capabilities, limitations, and applications, we gain insights into how researchers and practitioners can tailor underwater SLAM solutions to specific environmental conditions and mission requirements. Moreover, the integration of machine learning and deep learning methods for sensor data interpretation is steadily improving the adaptability and effectiveness of underwater SLAM systems. Ultimately, selecting the right combination of sensors and processing strategies is key to achieving accurate and efficient navigation and mapping under the challenging conditions of the underwater domain. Table 3 summarizes the advantages, disadvantages, and applications of vision, LiDAR, and sonar sensors for underwater SLAM applications.  
In summary, each sensor offers distinct advantages and limitations, and the choice depends on the specific requirements of the application and the environmental conditions.