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。
[Submitted on 20 Oct 2026] Title:Real-Time Ground Normal Vector Estimation for Spherical Mobile 3D Mapping using LiDAR-Inertial Odometry View a PDF of the paper titled Real-Time Ground Normal Vector Estimation for Spherical Mobile 3D Mapping using LiDAR-Inertial Odometry, by Tim Schubert and 2 other authors View PDF HTML (experimental) Abstract:Accurate ground plane normal estimation is essential for odometry in spherical mobile mapping systems (SMMS), which face challenges from rapid rotations and limited ground coverage. We propose two RANSAC-based approaches: single-scan estimation with normal vector scoring and multi-step validation, and LiDAR-inertial odometry (LIO)-based estimation using a global 3D map. Evaluated across diverse real-world datasets against terrestrial laser scanner ground truth, results show that LIO-based estimation significantly outperforms single-scan methods in accuracy and robustness. Integrating ground normal estimates into the motion model improves pose estimation, supporting autonomous navigation in challenging and hazardous environments. Comments: Proceedings of the International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS 26) Subjects: Robotics (cs.RO) Cite as: arXiv:2610.28701 [cs.RO] (or arXiv:2610.28701v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2610.28701 arXiv-issued DOI via DataCite Submission history From: Andreas Nüchter [view email] [v1] Tue, 20 Oct 2026 11:30:56 UTC (118,527 KB) Full-text links: Access Paper: View a PDF of the paper titled Real-Time Ground Normal Vector Estimation for Spherical Mobile 3D Mapping using LiDAR-Inertial Odometry, by Tim Schubert and 2 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 References & Citations SciX 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?)