Rigid Body Localization via Gaussian Belief Propagation with Quadratic Angle Approximation

[2602.04410] Rigid Body Localization via Gaussian Belief Propagation with Quadratic Angle Approximation Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Electrical Engineering and Systems Science > Signal Processing arXiv:2602.04410 (eess) [Submitted on 4 Feb 2026 ( v1 ), last revised 15 Aug 2026 (this version, v2)] Title: Rigid Body Localization via Gaussian Belief Propagation with Quadratic Angle Approximation Authors: Niclas Führling , Hyeon Seok Rou , Giuseppe Thadeu Freitas de Abreu , David González G. , Osvaldo Gonsa View a PDF of the paper titled Rigid Body Localization via Gaussian Belief Propagation with Quadratic Angle Approximation, by Niclas F\"uhrling and 4 other authors View PDF HTML (experimental) Abstract: Gaussian belief propagation (GaBP) is a technique that relies on linearized error and input output models to yield low-complexity solutions to complex estimation problems, which has been recently shown to be effective in the design of range-based GaBP schemes for stationary and moving rigid body localization (RBL) in three-dimensional (3D) space, as long as the relative rotation between the prior position and the target rigid body is sufficiently small. In this article we present a novel range-based RBL scheme via GaBP that relaxes the latter limitation significantly. To this end, the proposed method incorporates a quadratic angle approximation to linearize the relative orientation between the prior and the target rigid body, enabling high precision estimates of corresponding rotation angles even for large deviations. Leveraging the resulting linearized model, we derive the corresponding message-passing (MP) rules to obtain estimates of the translation vector and rotation matrix of the target rigid body, relative to a prior reference frame. Numerical results corroborate the good performance of the proposed angle approximation itself, as well as the consequent RBL performance in terms of root mean square errors (RMSEs) in comparison to the state-of-the-art (SotA), while maintaining a low computational complexity. Subjects: Signal Processing (eess.SP) Cite as: arXiv:2602.04410 [eess.SP] (or arXiv:2602.04410v2 [eess.SP] for this version) https://doi.org/10.48550/arXiv.2602.04410 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Niclas Führling [ view email ] [v1] Wed, 4 Feb 2026 10:39:52 UTC (296 KB) [v2] Sat, 15 Aug 2026 12:44:31 UTC (306 KB) Full-text links: Access Paper: View a PDF of the paper titled Rigid Body Localization via Gaussian Belief Propagation with Quadratic Angle Approximation, by Niclas F\"uhrling and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: eess.SP < prev | next > new | recent | 2026-02 Change to browse by: eess References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark 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? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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