# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 10 Conclusions  
This survey has provided a comprehensive examination of the integration of deep learning (DL) into simultaneous localization and mapping (SLAM) for underwater applications. As underwater navigation remains a challenge due to limited visibility, sensor noise, and the unpredictable nature of marine environments, traditional SLAM techniques have struggled to achieve robust and reliable performance. By leveraging DL, researchers have made significant strides in improving feature extraction, image enhancement, and sensor fusion, enabling more precise localization and mapping in underwater settings. Through a critical analysis of existing methodologies, this review has highlighted key advancements in DL-based underwater SLAM, including its role in enhancing front-end perception, mitigating environmental distortions, and improving loop closure detection. The discussion has also underscored the importance of integrating multi-modal sensor data, such as optical, sonar, acoustic, and inertial measurements, to compensate for the inherent limitations of individual sensors. Furthermore, this survey introduced a novel classification framework for underwater SLAM based on the integration of underwater wireless sensor networks (UWSNs), emphasizing the transformative potential of communication-aware SLAM systems. By leveraging distributed sensing and acoustic communication, UWSNs facilitate collaborative mapping and real-time localization among multiple autonomous underwater vehicles, leading to more scalable and resilient SLAM solutions. Despite these advancements, several challenges remain that require further exploration. The development of adaptive learning models capable of dynamically adjusting to variations in lighting, turbidity, and marine conditions is essential for ensuring consistent performance across different environments. Moreover, achieving real-time deployment of DL-based SLAM on computationally constrained platforms remains an open problem, necessitating the optimization of deep neural architectures for efficiency and energy conservation. Another pressing challenge is the need for long-term SLAM solutions that can maintain robustness over extended missions, addressing drift correction, large-scale mapping, and global consistency. Addressing these issues will be pivotal in driving the next generation of autonomous underwater robots capable of executing complex tasks with minimal human intervention. By synthesizing the latest research and identifying emerging trends, this survey serves as a valuable resource for researchers and practitioners working at the intersection of SLAM, deep learning, and underwater robotics. The continued evolution of DL-powered SLAM frameworks will play a crucial role in advancing underwater exploration, enabling more reliable navigation, resource mapping, and environmental monitoring. As the field progresses, further interdisciplinary collaboration will be key to overcoming existing barriers and unlocking new possibilities for autonomous operations in the vast and uncharted underwater world.  
M.H.: Original draft preparation, visualization, writing--review and editing; L.S.S.: Visualization, writing--review and editing; M.A.: Visualization; A.S.: Visualization, writing and editing; L.S.: review; LH.: Supervision. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Khalifa University of Science and Technology under Award CIRA-2021-085, Award No. 8434000534, Award No. KU-Stanford 8474000605 and Award RC1-2018-KUCARS.  
The authors declare no conflicts of interest.