# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 5 Sensors Driving Underwater SLAM
### Multi-Modal-Based SLAM  
Underwater environments often limit the effectiveness of single-sensor SLAM methods. Clear visibility may favor vision-based systems, but murky or low-light conditions can render optical sensors ineffective. Acoustic sensors, while robust in turbid waters, may struggle with fine details or rapidly changing scenes. LiDAR may provide precise distance measurements, but it can be challenged by aggressive vehicle motion or environments lacking distinctive features. To overcome these inherent trade-offs, researchers have increasingly turned to sensor fusion--also known as multi-modal SLAM--which integrates data from multiple sensor types to leverage their complementary strengths [43]. \begin{table}
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Multi-modal SLAM systems combine information from various modalities--such as cameras, sonar, LiDAR, IMUs, DVLs, and acoustic positioning systems like USBL--to produce more robust and comprehensive maps. By blending these diverse inputs, multi-modal SLAM can compensate for the weaknesses of each individual sensor. For example, pairing optical cameras with multibeam sonar improves 3D reconstruction and mapping accuracy in areas where vision alone would struggle [70]. Integrating inertial data helps stabilize pose estimates when visual or acoustic features are sparse, while LiDAR data can enhance detail and precision under challenging lighting conditions. Learning-based approaches have significantly advanced sensor fusion quality in underwater robotics. CNNs-based techniques enhance sonar imagery and leverage sensor complementarity, enabling more effective underwater perception and navigation [71]. Visual-inertial systems have also been adapted for underwater environments, integrating visual and inertial data to maintain reliable tracking even in challenging conditions such as fluctuating illumination and sparse features. For example, systems like USBL-aided navigation incorporate multiple sensory inputs--including VO and inertial measurements--to improve trajectory estimation [72]. Extensions to existing visual-inertial state estimation frameworks, such as integrating acoustic range data, have proven effective for reconstructing underwater structures in complex scenarios like caves and shipwrecks [73]. Furthermore, tightly coupled SLAM systems such as SVIn2 fuse sonar, visual, inertial, and water-pressure data, achieving robust initialization, loop closing, and localization under harsh underwater conditions, including haze, low light, and motion blur [74]. These innovations highlight the potential of sensor fusion to overcome the unique challenges of underwater environments. S. Ma et al. introduce a novel tightly coupled monocular-inertial-pressure (IP) sensor fusion method tailored for the underwater localization of a biomimetic robotic manta. Building on ORB-SLAM3 monocular visual-inertial odometry, depth measurements from a pressure sensor are incorporated, and a two-step monocular initialization strategy--first using visual-pressure (VP) measurements and then constructing inertial pressure depth residuals--significantly improves scale estimation. Following successful initialization, a visual-inertial-pressure (VIP) joint optimization enhances both position and attitude estimates, offering valuable insights for robust underwater localization of biomimetic robotic platform [75]. Y. Huang et al. explore advanced sensor fusion techniques that address scale drift and stability issues under variable lighting, turbidity, and acoustic conditions. By employing innovative data association strategies and refined sensor integration, this work provides further evidence that combining multiple sensors and careful calibration can yield more reliable navigation solutions across diverse underwater scenarios [76]. Multi-modal SLAM also benefits specialized applications such as underwater infrastructure inspection and ecological surveys.