Under the upsurge of research on the integration of Simultaneous Localization and Mapping (SLAM) and neural implicit representation, existing methods exhibit obvious limitations in terms of environmental semantic parsing and scene understanding capabilities. In response to this, this paper proposes a SLAM system that integrates a full attention mechanism and a multi-scale information extractor. This system constructs a more accurate 3D environmental model by fusing semantic, shape, and geometric orientation features. Meanwhile, to deeply excavate the semantic information in images, a pre-trained frozen 2D segmentation algorithm is employed to extract semantic features, providing a powerful support for 3D environmental reconstruction. Furthermore, a multi-layer perceptron and interpolation techniques are utilized to extract multi-scale features, distinguishing information at different scales. This enables the effective decoding of semantic, RGB, and Truncated Signed Distance Field (TSDF) values from the fused features, achieving high-quality information rendering. Experimental results demonstrate that this method significantly outperforms the baseline-based methods in terms of mapping and tracking accuracy on the Replica and ScanNet datasets. It also shows superior performance in semantic segmentation and real-time semantic mapping tasks, offering a new direction for the development of SLAM technology.
Liu et al. (2025) studied this question.