English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

A reliable, real time multi-sensor fusion functionality is crucial forlocalization of actively controlled capsule endoscopy robots, which are anemerging, minimally invasive diagnostic and therapeutic technology for thegastrointestinal (GI) tract. In this study, we propose a novel multi-sensorfusion approach based on a particle filter that incorporates an onlineestimation of sensor reliability and a non-linear kinematic model learned by arecurrent neural network. Our method sequentially estimates the true robot posefrom noisy pose observations delivered by multiple sensors. We experimentallytest the method using 5 degree-of-freedom (5-DoF) absolute pose measurement bya magnetic localization system and a 6-DoF relative pose measurement by visualodometry. In addition, the proposed method is capable of detecting and handlingsensor failures by ignoring corrupted data, providing the robustness expectedof a medical device. Detailed analyses and evaluations are presented usingex-vivo experiments on a porcine stomach model prove that our system achieveshigh translational and rotational accuracies for different types of endoscopiccapsule robot trajectories.

EndoSensorFusion: Particle Filtering-Based Multi-sensory Data Fusion with Switching State-Space Model for Endoscopic Capsule Robots