4,059 research outputs found
Unsupervised Odometry and Depth Learning for Endoscopic Capsule Robots
In the last decade, many medical companies and research groups have tried to
convert passive capsule endoscopes as an emerging and minimally invasive
diagnostic technology into actively steerable endoscopic capsule robots which
will provide more intuitive disease detection, targeted drug delivery and
biopsy-like operations in the gastrointestinal(GI) tract. In this study, we
introduce a fully unsupervised, real-time odometry and depth learner for
monocular endoscopic capsule robots. We establish the supervision by warping
view sequences and assigning the re-projection minimization to the loss
function, which we adopt in multi-view pose estimation and single-view depth
estimation network. Detailed quantitative and qualitative analyses of the
proposed framework performed on non-rigidly deformable ex-vivo porcine stomach
datasets proves the effectiveness of the method in terms of motion estimation
and depth recovery.Comment: submitted to IROS 201
Magnetic-Visual Sensor Fusion-based Dense 3D Reconstruction and Localization for Endoscopic Capsule Robots
Reliable and real-time 3D reconstruction and localization functionality is a
crucial prerequisite for the navigation of actively controlled capsule
endoscopic robots as an emerging, minimally invasive diagnostic and therapeutic
technology for use in the gastrointestinal (GI) tract. In this study, we
propose a fully dense, non-rigidly deformable, strictly real-time,
intraoperative map fusion approach for actively controlled endoscopic capsule
robot applications which combines magnetic and vision-based localization, with
non-rigid deformations based frame-to-model map fusion. The performance of the
proposed method is demonstrated using four different ex-vivo porcine stomach
models. Across different trajectories of varying speed and complexity, and four
different endoscopic cameras, the root mean square surface reconstruction
errors 1.58 to 2.17 cm.Comment: submitted to IROS 201
A Non-Rigid Map Fusion-Based RGB-Depth SLAM Method for Endoscopic Capsule Robots
In the gastrointestinal (GI) tract endoscopy field, ingestible wireless
capsule endoscopy is considered as a minimally invasive novel diagnostic
technology to inspect the entire GI tract and to diagnose various diseases and
pathologies. Since the development of this technology, medical device companies
and many groups have made significant progress to turn such passive capsule
endoscopes into robotic active capsule endoscopes to achieve almost all
functions of current active flexible endoscopes. However, the use of robotic
capsule endoscopy still has some challenges. One such challenge is the precise
localization of such active devices in 3D world, which is essential for a
precise three-dimensional (3D) mapping of the inner organ. A reliable 3D map of
the explored inner organ could assist the doctors to make more intuitive and
correct diagnosis. In this paper, we propose to our knowledge for the first
time in literature a visual simultaneous localization and mapping (SLAM) method
specifically developed for endoscopic capsule robots. The proposed RGB-Depth
SLAM method is capable of capturing comprehensive dense globally consistent
surfel-based maps of the inner organs explored by an endoscopic capsule robot
in real time. This is achieved by using dense frame-to-model camera tracking
and windowed surfelbased fusion coupled with frequent model refinement through
non-rigid surface deformations
Computer- and robot-assisted Medical Intervention
Medical robotics includes assistive devices used by the physician in order to
make his/her diagnostic or therapeutic practices easier and more efficient.
This chapter focuses on such systems. It introduces the general field of
Computer-Assisted Medical Interventions, its aims, its different components and
describes the place of robots in that context. The evolutions in terms of
general design and control paradigms in the development of medical robots are
presented and issues specific to that application domain are discussed. A view
of existing systems, on-going developments and future trends is given. A
case-study is detailed. Other types of robotic help in the medical environment
(such as for assisting a handicapped person, for rehabilitation of a patient or
for replacement of some damaged/suppressed limbs or organs) are out of the
scope of this chapter.Comment: Handbook of Automation, Shimon Nof (Ed.) (2009) 000-00
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