DSSM-SG: Dynamic 3D Scene Graphs with Spatio-Semantic Memory for Long-Term Indoor Navigation Tasks

Abstract

Dynamic indoor environments pose significant challenges for autonomous robots, as objects frequently move and scenes continuously change, requiring robust scene representation and adaptive navigation strategies. In this work, we introduce DSSM-SG, a dynamic open-vocabulary 3D scene graph framework enhanced with spatial-semantic memory, to support complex language instruction parsing and goal navigation in dynamic environments. First, we construct a multi-layered scene graph by combining waypoint topology with semantic object information, and propose a viewpoint-based mechanism to model object dynamics and detect scene changes, enabling more precise semantic-geometric representation. Second, we design an efficient incremental graph update strategy that adapts to object-level dynamics and navigation-observed obstacles, thereby maintaining graph consistency and alleviating mismatch during re-navigation. Finally, we introduce a subgraph generation and matching approach driven by large language models, significantly improving the system's ability to interpret and ground ambiguous goal descriptions. Experimental results demonstrate that DSSM-SG achieves superior performance in scene graph accuracy, update efficiency, and language goal navigation success compared to existing baselines in dynamic indoor environments.