ORBSLAM-based Endoscope Tracking and 3D Reconstruction

Int J Intell Robot Appl

Almalioglu

Araújo

et al. 2017

Since the development of capsule endoscopy technology, medical device companies and research groups have made significant progress to turn passive capsule endoscopes into robotic active capsule endoscopes. However, the use of robotic capsules in endoscopy still has some challenges. One such challenge is the precise localization of the actively controlled robot in real-time. In this paper, we propose a non-rigid map fusion based direct simultaneous localization and mapping method for endoscopic capsule robots. The proposed method achieves high accuracy for extensive evaluations of pose estimation and map reconstruction performed on a non-rigid, realistic surgical EsophagoGastroDuodenoscopy Simulator and outperforms state-of-the art methods.

Section: Visual Slam Structure and Surveymentioning

confidence: 99%

A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots

Int J Intell Robot Appl

Almalioglu

Araújo

et al. 2017

“…[30] adapted parallel tracking and mapping techniques to a stereo-endoscope to obtain reconstructed 3D maps that were denser when compared to monoscopic camera methods. [31] has applied ORB features to track the camera and proposed a method to densify the reconstructed 3D map, but pose estimation and map reconstruction are still not accurate enough. All of these methods can fail to produce accurate results in cases of low-texture areas, motion blur, specular highlights, and sensor noise -all of which are typically present during endoscopy.…”

Section: Introductionmentioning

confidence: 99%

“…[26] adapted parallel tracking and mapping techniques to a stereo-endoscope to obtain reconstructed 3D maps that were denser when compared to monoscopic camera methods. [27] has applied Fig. 2: Demonstration of the active endoscopic capsule robot operation using MASCE (Magnetically actuated soft capsule endoscope) designed for disease detection, drug delivery and biopsy-like operations in the upper GI-tract.…”

Section: Introductionmentioning

confidence: 99%

Magnetic- Visual Sensor Fusion-based Dense 3D Reconstruction and Localization for Endoscopic Capsule Robots

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Almalioglu

Örnek³

et al. 2018

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 realtime, 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.

“…demonstrating the efficiency of EKF-SLAM for hernia defect measurements in hernia repair surgery. Following the trend of PTAM modification, another milestone VSLAM method, ORB-SLAM was adjusted into medical field and proposed as ORB SLAM-based Endoscope Tracking and 3D Reconstruction method [20] for endoscopic localization.…”

Section: Introductionmentioning

confidence: 99%

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

Int J Intell Robot Appl

Shabbir

Araújo

et al. 2017

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.