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 10,007 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

In this paper, we present a novel method for intra-operative registration directly from monocular endoscopic images. This technique has the potential to provide a more accurate surface registration at the surgical site than existing methods. It can operate autonomously from as few as two images and can be particularly useful in revision cases where surgical landmarks may be absent. A by-product of video registration is an estimate of the local surface structure of the anatomy, thus providing the opportunity to dynamically update anatomical models as the surgery progresses. Our approach is based on a previously presented method [Burschka, D., Hager, G.D., 2004. V-GPS (SLAM):--Vision-based inertial system for mobile robots. In: Proceedings of ICRA, 409-415] for reconstruction of a scaled 3D model of the environment from unknown camera motion. We use this scaled reconstruction as input to a PCA-based algorithm that registers the reconstructed data to the CT data and recovers the scale and pose parameters of the camera in the coordinate frame of the CT scan. The result is used in an ICP registration step to refine the registration estimates. The details of our approach and the experimental results with a phantom of a human skull and a head of a pig cadaver are presented in this paper.
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PMID:Scale-invariant registration of monocular endoscopic images to CT-scans for sinus surgery. 1600 93

This paper presents a real-time, robust and low-drift depth-only SLAM (simultaneous localization and mapping) method for depth cameras by utilizing both dense range flow and sparse geometry features from sequential depth images. The proposed method is mainly composed of three optimization layers, namely Direct Depth layer, ICP (iterative closet point) Refined layer and Graph Optimization layer. The Direct Depth layer uses a range flow constraint equation to solve the fast 6-DOF (six degrees of freedom) frame-to-frame pose estimation problem. Then, the ICP Refined layer is used to reduce the local drift by applying local map based motion estimation strategy. After that, we propose a loop closure detection algorithm by extracting and matching sparse geometric features and construct a pose graph for the purpose of global pose optimization. We evaluate the performance of our method using benchmark datasets and real scene data. Experiment results show that our front-end algorithm clearly over performs the classic methods and our back-end algorithm is robust to find loop closures and reduce the global drift.
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PMID:Direct Depth SLAM: Sparse Geometric Feature Enhanced Direct Depth SLAM System for Low-Texture Environments. 3030 Nov 99