Closed-Form Pose Estimation of Endoluminal Medical Devices via Gradiometer-Based Electromagnetic Localization System
2026-06-01 • Robotics
Robotics
AI summaryⓘ
The authors propose a new system called GELS that helps track tiny magnetic devices inside the body without needing complicated maps or guesses about their starting point. Their method uses a special sensor array to measure magnetic fields and gradients, then mathematically figures out the device’s position and orientation using known magnetic sources. Tests show the system can locate devices with errors around 1 to 1.5 centimeters and updates quickly, making it promising for medical navigation. They also identified sources of error that can guide improvements in sensor and magnet design.
magnetic trackingpose recoverymagnetometer arraymagnetic field gradientEuler homogeneous relationProcrustes registrationdipole localizationendoluminal navigationnonlinear optimizationsensor calibration
Authors
Zhiwei Wu, Jiahao Luo, Yubo Pu, Siyi Wei, Yuankai Chen, Jinhui Zhang
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.