# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 1 Introduction
### Contributions  
This paper provides a detailed exploration of the evolution of SLAM methodologies, with a particular emphasis on the integration of deep learning techniques across different stages of the SLAM pipeline. Our primary contributions are as follows:  
* **Comprehensive review of DL-driven underwater SLAM methodologies:** We systematically analyze state-of-the-art approaches that leverage CNNs, transformers, and multi-modal sensor fusion techniques to enhance SLAM accuracy and robustness in underwater settings.
* **Identification and analysis of key challenges:** We investigate the major technical barriers in underwater SLAM, including lack of visibility, sensor drift, and acoustic communication constraints, providing insights into how DL methods address these issues.
* **Novel classification of SLAM approaches integrating UWSNs:** We introduce a new taxonomy for underwater SLAM based on its integration with UWSNs, emphasizing the role of distributed sensing and collaborative navigation in improving mapping accuracy and operational efficiency.
* **Critical evaluation and future research directions:** We outline open challenges and promising avenues for future research, including self-supervised learning for feature extraction, real-time deployment of lightweight DL models, and large-scale SLAM solutions for extended-duration underwater missions.  
By providing a structured synthesis of recent advancements, this survey serves as a key resource for researchers and practitioners in underwater robotics, deep learning, and autonomous navigation. Beyond addressing technical challenges, it highlights the broader significance of underwater SLAM, emphasizing its role in environmental monitoring, resource exploration, and multi-robot coordination. By bridging research gaps and showcasing the transformative impact of deep learning and networked SLAM techniques, this review paves the way for the next generation of autonomous underwater systems. Through an in-depth evaluation of state-of-the-art methodologies, we offer critical insights into the future of underwater robotics, driving innovation toward more reliable, efficient, and adaptive navigation in complex marine environments.  
The remainder of this paper is structured as follows. Section 2 provides an overview of the _core principles of SLAM_, introducing the SLAM front-end (feature-based and direct methods) and the SLAM back-end (filtering-based and optimization-based approaches). Section 3, _Underwater SLAM: Background and Overview_, discusses key challenges, tracing the evolution of underwater SLAM, and emphasizing the importance of UWSNs. Section 4 presents our _proposed SLAM classification based on UWSNs_, highlighting traditional classifications and introducing our new framework that integrates networking considerations. Section 5 focuses on the _sensors driving underwater SLAM_, including vision, acoustic, laser-based, and multi-modal sensing systems. Section 6 addresses the _practical challenges in underwater SLAM_, covering communication constraints, computational resources, and energy efficiency. Section 7 reviews _recent advances in deep learning-based SLAM_, examining feature extraction, pose estimation, loop closure detection, mapping, and 3D reconstruction, as well as integration with UWSNs and computational efficiency. Section 8 offersan _underwater SLAM systems evaluation and comparison_, discussing relevant metrics and comparing traditional versus DL-based methods. Section 9 identifies _research gaps and opportunities_, including the need for new datasets, the potential for deep reinforcement learning, improved 3D reconstruction and semantic mapping, large language model integration, dynamic environment handling, communication optimization in cooperative SLAM, new sensor technologies, and practical deployment challenges. Finally, Section 10, _Conclusions_, summarizes the key findings, reiterates the significance of the proposed UWSN-based classification, and provides a forward-looking perspective on underwater SLAM research.