A pre-explored metric-semantic topology with depth-grounded standoffs, multi-view fusion, and sequential verification achieves high success in repeated object navigation.
Topological Semantic Graph Memory for Image-Goal Navigation
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abstract
A novel framework is proposed to incrementally collect landmark-based graph memory and use the collected memory for image goal navigation. Given a target image to search, an embodied robot utilizes semantic memory to find the target in an unknown environment. % The semantic graph memory is collected from a panoramic observation of an RGB-D camera without knowing the robot's pose. In this paper, we present a topological semantic graph memory (TSGM), which consists of (1) a graph builder that takes the observed RGB-D image to construct a topological semantic graph, (2) a cross graph mixer module that takes the collected nodes to get contextual information, and (3) a memory decoder that takes the contextual memory as an input to find an action to the target. On the task of image goal navigation, TSGM significantly outperforms competitive baselines by +5.0-9.0% on the success rate and +7.0-23.5% on SPL, which means that the TSGM finds efficient paths. Additionally, we demonstrate our method on a mobile robot in real-world image goal scenarios.
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cs.RO 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
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SSTG-Nav: Metric-Grounded Spatial-Semantic Topological Graphs for Reusable Object Navigation
A pre-explored metric-semantic topology with depth-grounded standoffs, multi-view fusion, and sequential verification achieves high success in repeated object navigation.