Closed-Form Pose Estimation of Endoluminal Medical Devices via Gradiometer-Based Electromagnetic Localization System

• URL: http://arxiv.org/abs/2606.01946v1
• Date: Mon, 01 Jun 2026 09:09:04 GMT
• Title: Closed-Form Pose Estimation of Endoluminal Medical Devices via Gradiometer-Based Electromagnetic Localization System
• Abstract summary: This letter presents a closed-form tracking framework that uses a compact magnetometer array as an embedded quasi-gradiometer to estimate local magnetic fields and gradient tensors.<n>The algorithm requires known source positions and array geometry, but no pre-calibrated workspace field maps, initial pose guesses, or excitation-source moments.<n> Benchtop experiments across sensor-array configurations and excitation modes demonstrate sequence-averaged position errors of SI10.80millimeter--SI15.57millimeter, a fastest update rate of SI14.49hertz,
• Score: 3.4975218723249637
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Embedded magnetic tracking holds highly attractive prospects for remote navigation of endoluminal medical devices. However, existing six-degree-of-freedom pose recovery approaches often require pre-calibrated workspace field maps or iterative nonlinear optimization. This letter presents a Gradiometer-Based Electromagnetic Localization System (GELS), a closed-form tracking framework that uses a compact magnetometer array as an embedded quasi-gradiometer to estimate local magnetic fields and gradient tensors. These quantities are mapped by the Euler homogeneous relation to displacements between source and array, from which multi-source Procrustes registration recovers the array orientation and position using at least three non-collinear sources. The algorithm requires known source positions and array geometry, but no pre-calibrated workspace field maps, initial pose guesses, or calibrated excitation-source moments. The recovered pose also enables a proof-of-concept sub-level dipole localization task by serving as a mobile magnetic reference frame. Benchtop experiments across sensor-array configurations and excitation modes demonstrate sequence-averaged position errors of \SI{10.80}{\milli\meter}--\SI{15.57}{\milli\meter}, a fastest update rate of \SI{14.49}{\hertz}, and a median solver runtime of \SI{172.00}{\micro\second}. A perturbation-based error propagation analysis further identifies inter-sensor inconsistency and dipole-model mismatch as the dominant accuracy limits, thereby informing future sensor array and magnetic source design for further reducing pose-estimation error.

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