8 March 2013 Robust feature tracking for endoscopic pose estimation and structure recovery
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Abstract
Minimally invasive surgery is a highly complex medical discipline with several difficulties for the surgeon. To alleviate these difficulties, augmented reality can be used for intraoperative assistance. For visualization, the endoscope pose must be known which can be acquired with a SLAM (Simultaneous Localization and Mapping) approach using the endoscopic images. In this paper we focus on feature tracking for SLAM in minimally invasive surgery. Robust feature tracking and minimization of false correspondences is crucial for localizing the endoscope. As sensory input we use a stereo endoscope and evaluate different feature types in a developed SLAM framework. The accuracy of the endoscope pose estimation is validated with synthetic and ex vivo data. Furthermore we test the approach with in vivo image sequences from da Vinci interventions.
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S. Speidel, S. Speidel, S. Krappe, S. Krappe, S. Röhl, S. Röhl, S. Bodenstedt, S. Bodenstedt, B. Müller-Stich, B. Müller-Stich, R. Dillmann, R. Dillmann, } "Robust feature tracking for endoscopic pose estimation and structure recovery", Proc. SPIE 8671, Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling, 867102 (8 March 2013); doi: 10.1117/12.2007278; https://doi.org/10.1117/12.2007278
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