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翻訳待ち:Micro-Localization: High-Precision Mobile Robot Localization Using Microscopic Ground Texture

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AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要:arXiv:2610.22563v1 Announce Type: new Abstract: We demonstrate high-precision global localization using natural ground texture viewed through microscope optics, achieving localization in the tens-of-micrometers regime. For the planar ground texture localization problem considered here, we relate optical magnification and image-space localization uncertainty to physical localization precision. Leveraging this relationship, we then develop a microscope-based localization system that addresses the calibration, camera-modeling, and focus challenges introduced by high-magnification imaging. We evaluate this system across several surface textures using a calibrated precision gantry. Across 1,007 held-out queries per surface, the system localizes every que…

ソースarXiv Robotics著者: Aaron Wilhelm, Marina de Albuquerque Marcon, Emith Uyanwatte, Nils Napp
翻訳待ち:Micro-Localization: High-Precision Mobile Robot Localization Using Microscopic Ground Texture
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[Submitted on 15 Oct 2026] Title:Micro-Localization: High-Precision Mobile Robot Localization Using Microscopic Ground Texture View a PDF of the paper titled Micro-Localization: High-Precision Mobile Robot Localization Using Microscopic Ground Texture, by Aaron Wilhelm and 3 other authors View PDF HTML (experimental) Abstract:We demonstrate high-precision global localization using natural ground texture viewed through microscope optics, achieving localization in the tens-of-micrometers regime. For the planar ground texture localization problem considered here, we relate optical magnification and image-space localization uncertainty to physical localization precision. Leveraging this relationship, we then develop a microscope-based localization system that addresses the calibration, camera-modeling, and focus challenges introduced by high-magnification imaging. We evaluate this system across several surface textures using a calibrated precision gantry. Across 1,007 held-out queries per surface, the system localizes every query and achieves translation RMSEs of 28$-$73 $\mu$m. Independent photometric registration agrees with the feature-based localization estimates to within approximately 9 $\mu$m RMSE. We further demonstrate localization on a low-cost mobile robot using the gantry-collected maps, and also show that the system can resolve micrometer-scale displacement caused by thermal expansion. These results show that microscopic natural surface texture can bring mobile localization into a precision regime typically associated with fixed positioning systems. Subjects: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2610.22563 [cs.RO] (or arXiv:2610.22563v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2610.22563 arXiv-issued DOI via DataCite (pending registration) Submission history From: Aaron Wilhelm [view email] [v1] Thu, 15 Oct 2026 19:04:52 UTC (4,641 KB) Full-text links: Access Paper: View a PDF of the paper titled Micro-Localization: High-Precision Mobile Robot Localization Using Microscopic Ground Texture, by Aaron Wilhelm and 3 other authors View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.RO new | recent | 2026-10 Change to browse by: cs cs.CV References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)

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  • AI 生成が一時的に利用できないため、ソース内容とフォールバックメタデータを保存しました。
  • arXiv:2610.22563v1 Announce Type: new Abstract: We demonstrate high-precision global localization using natural ground texture viewed through microscope optics, achieving localiza…

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