# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 1 Introduction
### Motivations  
The study of underwater SLAM is not only a technical challenge but also a necessity for a range of real-world applications with significant scientific, economic, and environmental implications. Autonomous underwater exploration is crucial for tasks such as oceanographic mapping, deep-sea mining, disaster response, conducting inspections of underwater structures, and marine ecosystem conservation. However, reliable SLAM in underwater environments remains a formidable challenge due to fundamental constraints such as visibility degradation, sensor drift, and high localization uncertainty.  
Figure 1 shows that underwater SLAM research has been extensively published in leading robotics conferences and journals, underscoring its central importance. The growing interest in underwater SLAM is further reflected in the steady increase in SLAM-related  
publications over the past decade, as illustrated in Figure 2. This trend highlights the rapid advancements in underwater SLAM technologies and their expanding applications.  
Traditional SLAM methodologies, which rely on visual and geometric feature extraction, struggle to maintain accuracy in underwater conditions where textures are often repetitive, feature points are scarce, and sensor noise is prevalent. Moreover, acoustic-based localization, while promising, suffers from latency and bandwidth limitations. These issues necessitate a paradigm shift towards deep learning-enhanced SLAM, where neural networks can learn domain-specific features, improve sensor fusion, and enable robust mapping under extreme conditions.  
Beyond the technical challenges, underwater SLAM requires optimization for real-world deployment, where power efficiency, computational constraints, and adaptability to dynamic environments are critical factors. Many AUVs operate on limited battery resources, and real-time SLAM inference demands lightweight, energy-efficient DL models. Additionally, collaborative SLAM systems leveraging UWSNs offer new opportunities for large-scale, multi-robot navigation, but they introduce challenges related to communication latency and synchronization. Given these considerations, this survey is motivated by three key factors:  
* **The need for improved SLAM accuracy in extreme underwater conditions:** Enhancing robustness against visibility constraints, dynamic environments, and sensor noise is essential for real-world deployments.  
Figure 1: Number of publications in underwater SLAM research by publication venue.  
Figure 2: Number of publications per year in underwater SLAM research over the last decade.  
* **The growing role of deep learning in enhancing underwater SLAM pipelines:** DL-based techniques provide solutions for feature extraction, sensor fusion, and loop closure detection, outperforming traditional methods in challenging underwater conditions.
* **The integration of UWSNs for scalable, cooperative SLAM:** Multi-agent localization and distributed mapping offer promising improvements but require addressing communication constraints and synchronization challenges.