YOLO-NeRFSLAM: underwater object detection for the visual NeRF-SLAM

Accurate and reliable dense mapping is crucial for understanding and utilizing the marine environment in applications such as ecological monitoring, archaeological exploration, and autonomous underwater navigation. However, the underwater environment is highly dynamic: fish and floating debris frequently appear in the field of view, causing traditional SLAM to be easily disturbed during localization and mapping. In addition, common depth sensors and depth estimation techniques based on deep learning tend to be impractical or significantly less accurate underwater, failing to meet the demands of dense reconstruction. This paper proposes a new underwater SLAM framework that combines neural radiance fields (NeRF) with a dynamic masking module to address these issues. Through a Marine Motion Fusion (MMF) strategy—leveraging YOLO to detect known marine organisms and integrating optical flow for pixel-level motion analysis—we effectively screen out all dynamic objects, thus maintaining stable camera pose estimation and pixel-level dense reconstruction even without relying on depth data. Further, to cope with severe light attenuation and the dynamic nature of underwater scenes, we introduce specialized loss functions, enabling the reconstruction of underwater environments with realistic appearance and geometric detail even under high turbidity conditions. Experimental results show that our method significantly reduces localization drift caused by moving entities, improves dense mapping accuracy, and achieves favorable runtime efficiency in multiple real underwater video datasets, demonstrating both its potential and advanced capabilities in dynamic underwater settings.