Continuous Planning for Inertial-Aided Systems

Continuous Planning for Inertial-Aided Systems | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Continuous Planning for Inertial-Aided Systems Mitchell Usayiwevu, Fouad Sukkar, Chanyeol Yoo, Robert Fitch, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3990525/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Oct, 2024 Read the published version in Autonomous Robots → Version 1 posted 9 You are reading this latest preprint version Abstract Inertial-aided systems require continuous motion excitation among other reasons to characterize the measurement biases that will enable accurate integration required for localization frameworks. This paper proposes the use of informative path planning to find the best trajectory for minimizing the uncertainty of IMU biases and an adaptive traces method to guide the planner towards trajectories that aid convergence. The key contribution is a novel regression method based on Gaussian Process (GP) to enforce continuity and differentiability between waypoints from a variant of the RRT planning algorithm. We employ linear operators applied to the GP kernel function to infer not only continuous position trajectories, but also velocities and accelerations. The use of linear functionals enable velocity and acceleration constraints given by the IMU measurements to be imposed on the position GP model. The results from both simulation and real-world experiments show that planning for IMU bias convergence helps minimize localization errors in state estimation frameworks. Inertial-Aided Systems Localization IMU biases Gaussian Processes Full Text Additional Declarations No competing interests reported. Supplementary Files InformativePathPlanningbasedActiveLocalization.zip Cite Share Download PDF Status: Published Journal Publication published 12 Oct, 2024 Read the published version in Autonomous Robots → Version 1 posted Editorial decision: Revision requested 24 Jun, 2024 Reviews received at journal 15 Jun, 2024 Reviewers agreed at journal 03 May, 2024 Reviews received at journal 09 Apr, 2024 Reviewers agreed at journal 29 Mar, 2024 Reviewers invited by journal 29 Mar, 2024 Editor assigned by journal 17 Mar, 2024 Submission checks completed at journal 27 Feb, 2024 First submitted to journal 26 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3990525","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":275215117,"identity":"6fbcbffe-bbac-4923-98f8-0408c04ba9cc","order_by":0,"name":"Mitchell 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