# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 5 Sensors Driving Underwater SLAM
### Acoustic-Based SLAM  
Underwater environments often challenge visual sensors due to low visibility, limited light, and suspended particles that scatter or absorb light. In such conditions, acoustic sensors, especially sonar systems, provide a reliable alternative for SLAM tasks. Unlike optical methods, which depend on clarity and lighting, acoustic waves propagate efficiently \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Problem** & **Method** & **Findings** & **Limitations** \\ \hline Jung et al. (2017) [52] & SLAM for AUVs & \begin{tabular}{c} Vision-SLAM with \\ landmarks, IMU, DVL \\ \end{tabular} & \begin{tabular}{c} Enhanced visual \\ sensing \\ \end{tabular} & \begin{tabular}{c} Requires specific \\ sensors \\ \end{tabular} \\ \hline Joshi et al. (2023) [56] & \begin{tabular}{c} Robust estimation in \\ low visibility \\ \end{tabular} & \begin{tabular}{c} SM/VIO estimator \\ switching \\ \end{tabular} & \begin{tabular}{c} Tracked AUVs in \\ featureless env. \\ \end{tabular} & \begin{tabular}{c} Needs lighting \\ adaptation \\ \end{tabular} \\ \hline Lu et al. (2024) [53] & Navigation precision & \begin{tabular}{c} ORB-SLAM3-VIP \\ fusion \\ \end{tabular} & 42\% error reduction & \begin{tabular}{c} Needs extra \\ initialization \\ \end{tabular} \\ \hline Ou et al. (2024) [51] & \begin{tabular}{c} Passive V-SLAM \\ improvement \\ \end{tabular} & Hybrid-VINS & \begin{tabular}{c} Outperformed passive \\ setups \\ \end{tabular} & Slow startup \\ \hline Liu et al. (2024) [54] & Sediment visibility loss & \begin{tabular}{c} Adaptive filtering \\ V-SLAM \\ \end{tabular} & \
\begin{tabular}{c} Better than \\ ORB-SLAM3 \\ \end{tabular} &
\begin{tabular}{c} Inflexible parameters \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Selected vision-based underwater SLAM systems. in water, allowing sonar-based approaches to work effectively in murky, deep, or low-visibility environments [34]. Acoustic SLAM leverages sonar signals--ranging from single-beam and side-scan to multi-beam configurations--to navigate and map underwater areas (see Figure 10). Because acoustic waves can travel long distances with minimal attenuation, sonar-based methods are particularly valuable for large-scale mapping, deep-sea exploration, underwater infrastructure inspection, and search and rescue operations. They remain robust when visual cues are absent or severely diminished, offering a distinct advantage over vision-based approaches [43]. However, acoustic systems are not without their challenges. Sonar data can suffer from noise interference, and their spatial resolution generally lags behind that of high-quality optical sensors. Consequently, acoustic SLAM maps may lack the fine detail provided by vision-based methods. Additionally, acoustic noise, multi-path reflections, and complex signal processing requirements can complicate data interpretation, increasing the operational complexity and cost of deploying and maintaining these systems [58]. Recent advancements have significantly enhanced the effectiveness of acoustic SLAM, addressing challenges like poor bearing accuracy and resolution. Researchers have adapted algorithms such as FastSLAM variants to better process sonar data, while techniques like CNN-based sonar image matching have achieved superior accuracy compared to classical methods [59; 60]. Low-cost forward-looking sonar systems have demonstrated feasibility for navigation and feature reacquisition using innovative SLAM approaches like pose-graph optimization [61]. Similarly, enhanced loop closure detection methods utilizing acoustic image segmentation and graph-based models have improved mapping robustness in real-world scenarios [62]. Filter-based methodologies, such as RBPF-SLAM, effectively manage data-intensive sonar environments, while YOLOv7 applications to 3D reconstruction further refine state estimation and mapping accuracy [63; 64].