# 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
The method utilizes a neural network to produce the covariance matrix through the parameter estimation of Cholesky Decomposition. Additionally, a CNN-based method [117] has been developed to handle unreliable keypoints caused by shallow water caustics and dynamic objects. This method filters out unreliable points within a VSLAM framework, enhancing robustness. The MARESye system [130] exemplifies how dense 3D data can be captured in visually challenging underwater environments through active and passive imaging. Multi-modal SLAM frameworks thus enhance overall reliability, making them invaluable for underwater exploration and infrastructure inspections. DL has also been utilized to apply image enhancement for robust feature extraction in low-light, blurry, and noisy underwater images. For example, a CNN-based end-to-end network [127] was developed to tackle low-light environments, incorporating a self-supervised feature point detector and descriptor that enables VSLAM to operate in low-light conditions without requiring paired training data. Similarly, Wang et al. [128] propose a robust DL-based VSLAM system featuring UWNet, a powerful feature generator that extracts accurate keypoints and utilizes knowledge distillation for training. Integrated with ORB-SLAM3, the system demonstrates high precision and robustness in public and self-collected datasets, significantly improving performance in complex underwater scenarios. Generative adversarial networks (GANs) are also used for underwater image enhancement, further improving the performance of SLAM systems in challenging underwater conditions [129]. For nullifying the effect of marine snow noise, Hodne et al. [126] develop two efficient  
Figure 11: Typical pipeline for feature extraction in DL-based underwater SLAM. The process includes pre-processing to remove marine snow [126], enhance light conditions [127], and reduce underwater noise such as color distortions and blurring [128]. Keypoints are then extracted using CNNs, filtered, and matched. (Adapted from Zheng et al. [129]). classifiers that run on top of arbitrary keypoint detectors to classify marine snow and subsequently reject it before feature extraction. Furthermore, CNNs can extract features from fused sonar and camera images, improving perception, obstacle avoidance, and environmental mapping. For instance, UAFMFDet [71] is a dual-branch CNN for acoustic-optical fusion-based object detection, which showed significant improvement compared to other object detection methods. Moreover, sonars can also be utilized for underwater dynamic-SLAM to handle dynamic objects, significantly improving SLAM's capabilities in underwater contexts, as shown in [131]. The utilized method leverages YOLOv3 in conjunction with a multi-beam sonar for underwater dynamic tracking. In summary, feature extraction for underwater SLAM is an active research area. With advances in DL, uncertainty estimation, and keypoint selection methods, researchers are developing innovative solutions to address the unique challenges of underwater environments.