# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 3 Underwater SLAM: Background and Overview
### Evolution of Underwater SLAM  
Despite substantial advancements in SLAM algorithms, their adaptation for underwater applications has also progressed. Figure 6 highlights the different stages in the progression of underwater SLAM, from traditional geometric approaches to advanced deep learning-based techniques. The early stages of SLAM development before 2000 focused on fundamental concepts and basic implementations, laying the groundwork for future advancements. Between 2000 and 2010, underwater SLAM emerged as a distinct research area, driven by the increasing need for autonomous navigation in marine environments.  
From 2010 to 2015, advancements in algorithms and sensor technologies significantly improved the reliability and accuracy of SLAM systems. This period saw the integration of novel sensors, such as acoustic and vision-based systems, tailored to the specific challenges of underwater environments. Between 2015 and 2020, incorporating deep learning and using more sophisticated underwater robots marked a transformative shift in SLAM capabilities. Deep learning enabled more robust feature extraction, data processing, and decision making, significantly enhancing SLAM performance under these challenging conditions.  
The present stage, characterized by emerging and future technologies, focuses on leveraging advanced DL technologies, multi-modal sensor fusion, real-time processing, and collaborative systems to address the remaining limitations of underwater SLAM. Advancing SLAM for underwater use is crucial for addressing the unique challenges posed by underwater settings ultimately driving progress in autonomous underwater exploration and sustainable resource management [27].  
The evolution of underwater SLAM reflects a transition from traditional methods to advanced, adaptive techniques tailored for aquatic environments. Early SLAM approaches, such as EKF SLAM and graph-based SLAM, were initially developed for terrestrial robots operating in structured environments with abundant features and stable illumination [32; 33]. However, these methods often struggled underwater due to low visibility, sensor noise, and dynamic conditions, leading to degraded accuracy and reliability.  
To address these limitations, researchers began refining traditional SLAM methods for underwater applications. For instance, multi-sensor fusion techniques incorporating inertial measurement units (IMUs), Doppler velocity logs (DVLs), and acoustic sonars were developed to compensate for unreliable visual data and stabilize pose estimates [34; 35]. Filtering-based methods like EKF-SLAM and particle filter-based SLAM were adapted to handle acoustic measurements and nonlinear motion, while optimization-based methods integrated acoustic range constraints and sonar-based loop closures [36].  
Bioinspired algorithms also emerged, drawing inspiration from marine life to navigate noisy, low-visibility environments effectively [36]. These advancements enabled SLAM systems to adapt to diverse underwater environments, ranging from structured settings, pipelines, and seabed infrastructure to unstructured or dynamic scenarios, coral reefs, and open water, [37]. Table 2 provides a summary of traditional SLAM methods used in underwater applications, highlighting their sensors, approaches, and limitations.  
Figure 6: Different stages of underwater SLAM development, from traditional to deep learning-based methods.  
As underwater SLAM continues to evolve, the role of UWSNs has become increasingly important. UWSNs provide a framework for distributed sensing, communication, and data sharing, addressing many of the challenges associated with underwater environments. The integration of deep learning and UWSNs has enabled more accurate data and effective monitoring of underwater environments [38]. The next subsection explores the significance of UWSNs and their potential to revolutionize underwater SLAM.