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Article detail · 2018 · conference-paper

Endo-VMFuseNet: A Deep Visual-Magnetic Sensor Fusion Approach for Endoscopic Capsule Robots

OpenAlex Open access · green Top 10%
Year2018
Citations12OpenAlex
Percentile%93.2
FWCI3.371.00 = world average

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  • OpenAlexOpenAlex enrichment (abstract, citations, topics)

Abstract

OpenAlex English

In the last decade, researchers and medical device companies have made major advances towards transforming passive capsule endoscopes into active medical robots. One of the major challenges is to endow capsule robots with accurate perception of the environment inside the human body, which will provide necessary information and enable improved medical procedures. We extend the success of deep learning approaches from various research fields to the problem of sensor fusion for endoscopic capsule robots in the case of asynchronous and asymmetric sensor data without any need of calibration between sensors. The results performed on real pig stomach datasets show that our method achieves high precision for both translational and rotational movements and contains various advantages over traditional sensor fusion techniques.

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Citations

OpenAlex cited_by_count. Not a WoS or Scopus citation count; those sources have no separate column here.

12citationsOpenAlex · cited_by_count (cache / database)

2 publications in the local catalog that cite this work (OpenAlex reference match; not the full global list).

  1. 2022 SelfVIO: Self-supervised deep monocular Visual\u2013Inertial Odometry and depth estimationCitations 111 · OpenAlex
  2. 2017 Sparse-then-dense alignment-based 3D map reconstruction method for endoscopic capsule robotsCitations 36 · OpenAlex

Authors

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