USO-SLAM: SLAM With Single-Frame Semantic Object Reconstruction for Autonomous Underwater Vehicles

Simultaneous localization and mapping (SLAM) based on point features is widely used in precision missions for unmanned underwater vehicles (UUVs). However, the inherent complexity of underwater environments poses significant challenges for point feature-based SLAM, such as reduced pose estimation accuracy due to factors like turbidity and weak texture, as well as limited semantic information conveyed by sparse point cloud maps. To address these challenges, this study introduces an innovative approach termed underwater semantic object SLAM (USO-SLAM), based on stereo camera. We propose a novel algorithm designed for reconstructing objects that naturally occur underwater, thereby substantially enhancing reconstruction success rate and object pose estimation accuracy. Furthermore, we present a comprehensive underwater semantic object SLAM algorithmic framework, incorporating a robust semantic loop closure module whereby objects are encoded within a two-layers topological graph structure. Empirical findings derived from both simulated and real-world underwater environments corroborate that our proposed method outperforms existing state-of-the-art semantic reconstruction algorithms and SLAM systems, particularly in terms of semantic object reconstruction and navigation accuracy in underwater settings.