Reproduce “A Sparse Topometric Graph for Scalable Cross-Session Indoor Navigation” on OpenResearch

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About this paper

Eun Sun Lee, Minkyun Seo, Gwangtak Bae, Junho Kim, Seunggu Kang, Jaesik Park, Young Min Kim

The authors introduce a sparse topometric map of traversable locations, using geometry descriptors and connectivity in a learned matcher to align partial maps and transfer goals across sessions. In Gibson navigation, it reaches a 77.14% success rate, versus 54.28% for the next-highest baseline. On 3D-FRONT, it reaches 40% under a 1 m threshold; tests cover single-floor 2D navigation, not real-world deployment.

Summary by alphaXiv.

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