# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 6 Practical Challenges in Underwater SLAM
### Energy Efficiency  
Underwater robots often operate far from any direct power source, making energy efficiency a key requirement for extended missions. Propulsion already consumes a substantial share of available power; onboard SLAM computations add further strain [29]. Cameras, sonar arrays, and high-power illumination also contribute to the overall energy budget [107]. As a result, frequent battery recharges or replacements become logistically and economically challenging, particularly in remote or deep-sea environments. Achieving high SLAM accuracy often involves running computationally expensive models at higher frame rates or resolutions [108]. However, each additional network parameter or sensor input can significantly increase power consumption. Techniques like dynamic frame rate adaptation, where the SLAM process lowers frame capture rate in less complex areas, can reduce energy usage while maintaining adequate map quality [42]. Similarly, adopting specialized low-power hardware such as FPGAs or efficient GPU cores can sustain more advanced DL models within the same energy budget. Some recent research aims to integrate energy considerations directly into the SLAM loop, dynamically balancing exploration and revisiting tasks [109]. By incorporating energy models that account for sensor usage, processor clock speeds, and propulsion, the SLAM system can decide when to switch sensors on/off or how aggressively to update the map. In multi-AUV missions, coordinating battery levels and assigning tasks based on remaining energy further extends mission duration [110]. One promising energy-aware method is dynamic sensor scheduling, which involves selectively activating or deactivating sensors based on environmental conditions and mission requirements. For example, in underwater environments with turbidity or low light, the SLAM system can prioritize sonar data over high-resolution visual inputs, reducing the energy consumed by power-intensive cameras. Conversely, in lit conditions, visual sensors can take precedence to enhance mapping accuracy. Another approach is to adjust the sampling rate of sensors dynamically: Lowering the frame rate of cameras when the robot is stationary or navigating well-mapped areas can significantly cut power usage without sacrificing map quality. Existing work, such as reinforcement learning-based sensor management, offers a framework for learning optimal activation policies that balance energy efficiency and localization performance [111]. These techniques are particularly valuable in underwater SLAM, where variable conditions demand adaptive sensor use. Another key energy-aware strategy is adaptive DL model compression, which adjusts the complexity of deep learning models in real time to optimize energy consumption while preserving accuracy. Techniques such as model pruning, removing less critical network parameters, or quantization, reducing the precision of model weights, can be applied dynamically based on the current energy budget or computational load. For instance, during routine mapping in familiar areas, a lightweight, pruned model can suffice, whereas a full model might be activated for challenging tasks like loop closure detection in uncharted regions. A multi-model approach could also be employed, where the SLAM system switches between a library of pre-trained models of varying complexity depending on the task or remaining battery level. Frameworks like once-for-all [112] enable such dynamic model selection by training a single supernet from which sub-models can be extracted efficiently, adapting to resource constraints on the fly. These methods reduce the computational burden of DL-based SLAM, making them better suited for energy-constrained underwater missions. While compression techniques like pruning or KD reduce model size, energy efficiency can also benefit from hardware improvements and optimized scheduling policies that dynamically turn off non-critical components.