# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 1 Introduction  
The vast and largely unexplored marine environment plays a pivotal role in scientific exploration, resource management, and environmental conservation. Autonomousunderwater vehicles (AUVs) are essential for a wide range of applications, including coral reef monitoring, offshore infrastructure inspection, deep-sea exploration, and underwater archaeology. A fundamental requirement for these robotic systems is robust and precise navigation, which is largely enabled by simultaneous localization and mapping (SLAM). SLAM enables AUVs to construct spatial maps while simultaneously estimating their position within the environment [1]. This capability is crucial for executing complex missions with minimal human intervention, particularly in hazardous or remote underwater settings.  
Despite the success of SLAM in terrestrial environments, its adaptation to underwater domains presents significant challenges. These include low visibility due to light absorption and scattering, sensor noise, dynamic lighting conditions, and distortions caused by the water column [2]. Unlike in air, where visual and LiDAR-based SLAM systems are widely used, underwater SLAM relies on a combination of sonar, acoustic signals, inertial sensors, and vision-based approaches, each with inherent limitations. These constraints degrade the accuracy of feature extraction, loop closure detection, and trajectory estimation, which are critical components of SLAM pipelines.  
To mitigate these challenges, deep learning (DL) has emerged as a transformative tool in underwater SLAM. Convolutional neural networks (CNNs) and transformer-based architectures have demonstrated remarkable improvements in feature extraction, denoising, and data-driven sensor fusion, addressing many limitations of traditional methods [3]. DL-powered models facilitate robust perception in low-visibility conditions, enhance map consistency, and improve loop closure detection, significantly augmenting SLAM performance in underwater environments [4]. Additionally, multi-modal approaches integrating vision, sonar, and inertial data have proven effective in compensating for sensor deficiencies, enabling more resilient localization and mapping systems.  
In addition to enhancing SLAM, an emerging research direction is the integration of underwater wireless sensor networks (UWSNs). UWSNs provide a distributed sensing framework where multiple AUVs and sensor nodes collaboratively share localization and mapping data via acoustic communication [5]. By incorporating UWSN-based SLAM, navigation accuracy and robustness can be improved, especially in large-scale, multi-agent underwater operations.  
This survey presents a comprehensive review of DL-driven underwater SLAM, addressing key challenges, recent innovations, and future research directions. We propose a novel classification framework that integrates UWSN-based SLAM methodologies, highlighting their impact on collaborative navigation and large-scale mapping. By synthesizing the latest advancements in deep learning, sensor fusion, and communication-aware SLAM, this work aims to bridge the gap between theoretical research and practical applications, paving the way for next-generation autonomous underwater navigation systems.