MTE-SLAM: Multi-Tier Feature Fusion for Efficient Neural Semantic SLAM

Abstract

Neural implicit representations have demonstrated excellent performance in Simultaneous Localization and Mapping (SLAM) by virtue of their ability to jointly model geometry, color and camera poses. Recent studies have attempted to integrate scene semantic information into implicit representation frameworks, significantly improving the ability of environmental understanding. Nevertheless, most existing methods rely on direct semantic coloring or rough fusing other modalities, resulting in underutilized semantic clues. This further causes problems such as blurred small objects, loss of fine structures and unclear regional boundaries. Additionally, redundant features introduced in the process reduce system efficiency. To address these challenges, we propose MTE-SLAM, an accurate and efficient end-to-end neural RGB-D semantic SLAM framework that synergizes Multi-Tier Feature Fusion (MTFF) and Feature Redundancy Suppressor (FRS). MTFF progressively fuses semantic features at global and local scales. The global context enhancement module captures scene-level semantic correlations, while the local continuity enhancement module refines neighborhood consistency, generating detailed and coherent semantic maps. FRS adaptively filters redundant features based on their importance and temporal variation, reducing parameters and computation while preserving representational power to accelerate training and inference. Comprehensive evaluations on Replica and ScanNet demonstrate that MTE-SLAM achieves centimeter-level reconstruction, state-of-the-art tracking and semantic accuracy, and runs up to four times faster than existing semantic SLAM systems.