Self-Sufficient 5-DoF Discrete Global Localization for Magnetically-Actuated Endoscope in Bronchoscopy
Jiewen Tan, Da Zhao, Rui Zhou, Wenxuan Xie, Shing Shin Cheng
Abstract
Existing sensor-based global localization methods limit the miniaturization potential of magnetically-actuated endoscopes (MAE) while localization based on external medical imaging demands accurate registration and imposes a variety of modality-specific challenges during continuous image acquisition. This work proposes a novel self-sufficient method for discrete (one-time) global localization of an MAE based solely on inherent endoscopic images without any prior MAE pose information. More specifically, it adopts a model-free control approach to determine five different external magnet (EM) poses (corresponding to five independent nonlinear equations) that can align the MAE image center with the lumen center while the MAE maintains the same pose. The five degree-of-freedom (DoF) global pose of the MAE can then be estimated by minimizing the root mean square of MAE's torque balance residuals under these EM poses. Our proposed method achieves similar accuracy as other sensor-based methods for permanent magnet-driven MAE with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{6.7} \pm \mathbf{2.1}$</tex> mm position error and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9.5} \pm \mathbf{2.9}^{\circ}$</tex> orientation error in the experiments. Compared to existing methods, our approach does not require physical sensor integration, enabling a more compact endoscope design for exploration in narrower respiratory tracts. It also offers a critical step toward achieving sensorless and continuous global localization of the permanent magnet-driven MAE during its autonomous navigation.
BibTeX
@inproceedings{icra2025_selfsufficient5d,
title = {Self-Sufficient 5-DoF Discrete Global Localization for Magnetically-Actuated Endoscope in Bronchoscopy},
author = {Jiewen Tan and Da Zhao and Rui Zhou and Wenxuan Xie and Shing Shin Cheng},
booktitle = {ICRA 2025},
year = {2025}
}