Eun Sun Lee, Minkyun Seo, Gwangtak Bae, Junho Kim, Seunggu Kang, Jaesik Park, Young Min Kim · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.31432 · cited by 0 works (OpenAlex) · open-access PDF
एक पारंपरिक लक्ष्य स्थानांतरण एजेंट एक आंतरिक वातावरण में एक मजबूत लक्ष्य स्थानांतरण स्थानांतरण एक नक्शा बनाता है ध्वनि वास्तविक दुनिया मापों और भारी निष्कर्ष से मार्गदर्शन नेविगेशन के परिणामों को जमा करके एक नक्शा बनाता है. हालांकि विस्तृत माप दृश्य परिवर्तनों या सेंसर सेटिंग्स के कारण भिन्न हो सकते हैं, एक अद्वितीय टोपोलॉजिकल संरचना नेविगेट पारंपरिक पारंपरिक पारंपरिक पारंपरिक या कमरों के बीच कनेक्शन को प्रशिक्षण मुक्त मार्गदर्शन प्रदान कर सकता है जो स्थानीय निरीक्षणों को पूरक करता है. हम एक कठोर टोपोमेट्रिक ग्राफिक्स को एक कुशल संरचना प्रतिनिधित्व के रूप में प्रस्तावित करते हैं जो मजबूत
Machine translation (Meta M2M100); the English original follows.
A conventional embodied agent in an indoor environment builds a map by accumulating noisy real-world measurements and results from heavy inference to guide navigation. While the detailed measurements may differ due to scene changes or sensor settings, the unique topological structure of navigable passages or the connectivity between rooms may provide training-free guidance that complements local observations. We propose a sparse topometric graph as an efficient structural representation for robust indoor navigation, alleviating the need for fine-grained matching, which is computationally expensive, memory-intensive, and prone to real-world variations. The map consists of nodes that encode local 3D geometry and distance-weighted edges that capture traversable connectivity. A learned graph-matching network finds consistent subgraph structures across sessions by considering both node-level geometric descriptors and neighborhood connectivity. The holistic contextual matching is robust to local ambiguities caused by weak distinctiveness or repetitive structure, and establishes stable correspondences despite local scene changes, different sensor modalities, or limited spatial overlap. We analyze the practicality of the graph through cross-session point-goal navigation, where a goal specified in a prior reference map must be reached from an unknown initial pose in a later session. Experiments show that our method achieves the highest success rates (77.1% on Gibson and 40.0% under 3D-FRONT scene changes), while reducing memory by more than two orders of magnitude compared to point-level representations. The proposed approach further supports cross-session alignment and goal-directed navigation with strong scalability and robust real-world transfer.
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Topics: Computer science, Scalability, ENCODE, Inference, Artificial intelligence, Graph
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