A deep learning based fusion of RGB camera information and magnetic localization information for endoscopic capsule robots

被引：22

作者：

Turan M. ^{[1
]}

Shabbir J. ^{[2
]}

Araujo H. ^{[2
]}

Konukoglu E. ^{[3
]}

Sitti M. ^{[1
]}

机构：

[1] Physical Intelligence Department, Max Planck Institute for Intelligent Systems, Stuttgart

[2] Institute for Systems and Robotics, Universidade de Coimbra, Coimbra

[3] Computer Vision Laboratory, ETH Zurich, Zurich

来源：

International Journal of Intelligent Robotics and Applications | 2017年 / 1卷 / 4期

关键词：

Deep Learning based Sensor Fusion; Endoscopic Capsule Robots; RNN-CNN (RNN:Recurrent Neural Network; CNN: Convolutional Neural Network);

D O I：

10.1007/s41315-017-0039-1

中图分类号：

学科分类号：

摘要：

A reliable, real time localization functionality is crutial for actively controlled capsule endoscopy robots, which are an emerging, minimally invasive diagnostic and therapeutic technology for the gastrointestinal (GI) tract. In this study, we extend the success of deep learning approaches from various research fields to the problem of sensor fusion for endoscopic capsule robots. We propose a multi-sensor fusion based localization approach which combines endoscopic camera information and magnetic sensor based localization information. The results performed on real pig stomach dataset show that our method achieves sub-millimeter precision for both translational and rotational movements. © 2017, The Author(s).

引用

页码：442 / 450

页数：8

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