# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 7 Recent Advances in Deep Learning-Based SLAM
### Underwater Feature Extraction for SLAM  
Unlike terrestrial environments, extracting meaningful features from underwater scenes presents significant challenges for VSLAM due to low-light conditions, color distortions, blurring, and unreliable keypoints. To address these challenges, recent research leverages ML. One prominent approach involves utilizing supervised learning to identify high-level structural features, enabling AUVs to effectively relocalize within a SLAM graph [123]. Traditional feature extraction methods, such as SIFT, SURF, and ORB which are widely used in terrestrial SLAM, struggle in underwater environments. These handcrafted feature detectors rely on gradient-based keypoints that become unreliable in turbid or noisy conditions, often resulting in low feature matching accuracy. In contrast, DL methods learn robust, data-driven features from large, diverse datasets. This improvement stems from the ability to adapt to varying visibility and lighting through training, offering superior generalization compared to the rigid, predefined rules of traditional techniques. However, DL approaches require substantial computational resources and annotated training data, presenting trade-offs that traditional methods avoid despite their lower performance. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Reference** & **Problem** & **Method** & **DL Architecture** & **Findings** & **Limitations** \\ \hline Burguera et al. (2020)[115] & VSLAM & Unsupervised neural network & Autoencoder & Improved feature matching and pose estimation & May not scale well with large datasets \\ \hline Torroba et al. (2020)[116] & SLAM point cloud registration & PointNet-based covariance estimation & PointNet & Enhanced point cloud registration accuracy & May require large datasets \\ \hline Leonardi et al. (2020)[117] & VSLAM & CNN-based keypoint rejection system & CNN & Increased robustness in VSLAM & May not handle all types of keypoint failures \\ \hline Marques et al. (2019)[118] & VSLAM & GAN-based depth estimation & GAN & Achieves trajectory error of 1.6 feet in subsea test dataset & Requires a dataset of videos \\ \hline Burguera et al. (2022)[119] & Graph VSLAM & Outlier-resilient visual graph-SLAM & Siamese CNN & Reduces false loop closures & Has not been tested on AUV in real environment \\ \hline Li et al. (2018)[120] & Real-time SLAM &
\begin{tabular}{c} Sonar imaging-based saliency-aware loop closure \\ \end{tabular} & CNN & Reliable data association & Specific to certain environments \\ \hline Wang et al. (2022)[121] & Visual loop closure detection & Variational autoencoder network & VAE & Achieved recall rate of 92.31\% in underwater dataset & Has not been tested on AUV in real environment \\ \hline Tan et al. (2023)[122] & SLAM & Data-driven loop closure detection & Siamese CNN & Provides loop closure method and bathymetric dataset & Has not been tested in real environment \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Summary of selected DL-based underwater SLAM: problem, method, DL architecture, findings, and limitations. Several other studies demonstrate the effectiveness of DL in underwater feature extraction for robot navigation, as shown in Figure 11. These methods mostly use CNNs to learn compact representations. Maldonado-Ramirez et al. [124] employs convolutional autoencoders to extract salient landmarks from underwater images, improving precision and inference time for underwater SLAM. The findings show improved performance in terms of precision and inference time. Similarly, Peng et al. [125] propose PointNet, a multi-layer perceptron network that receives selected keypoints from a K-nearest neighbor algorithm to extract relevant features. Reliable feature extraction also requires addressing uncertainty and keypoint selection. A PointNet-based approach for uncertainty estimation in point cloud registration, called PointNetKL [116], has been proposed to address this, offering a computationally efficient alternative to traditional Monte Carlo methods.