Real-time simultaneously localization and dense mapping is very helpful for providing Virtual Reality and Augmented Reality for surgeons or even surgical robots. In this paper, we propose MIS-SLAM: a complete real-time large scale dense deformable SLAM system with stereoscope in Minimal Invasive Surgery based on heterogeneous computing by making full use of CPU and GPU. Idled CPU is used to perform ORB-SLAM for providing robust global pose. Strategies are taken to integrate modules from CPU and GPU. We solved the key problem raised in previous work, that is, fast movement of scope and blurry images make the scope tracking fail. Benefiting from improved localization, MIS-SLAM can achieve large scale scope localizing and dense mapping in real-time. It transforms and deforms current model and incrementally fuses new observation while keeping vivid texture. In-vivo experiments conducted on publicly available datasets presented in the form of videos demonstrate the feasibility and practicality of MIS-SLAM for potential clinical purpose.
The main contribution of this paper is a novel feature parametrization based on parallax angles for bundle adjustment (BA) in structure and motion estimation from monocular images. It is demonstrated that under certain conditions, describing feature locations using their Euclidean XYZ coordinates or using inverse depth in BA leads to ill-conditioned normal equations as well as objective functions that have very small gradients with respect to some of the parameters describing feature locations. The proposed parallax angle feature parametrization in BA (ParallaxBA), avoids both of the above problems leading to better convergence properties and more accurate motion and structure estimates. Simulation and experimental datasets are used to demonstrate the impact of different feature parametrizations on BA, and the improved convergence, efficiency and accuracy of the proposed ParallaxBA algorithm when compared with some existing BA packages such as SBA, sSBA and g2o. The C/C++ source code of ParallaxBA is available on OpenSLAM (https://openslam.org/).
In minimal invasive surgery, it is important to rebuild and visualize the latest deformed shape of soft-tissue surfaces to mitigate tissue damages. This paper proposes an innovative Simultaneous Localization and Mapping (SLAM) algorithm for deformable dense reconstruction of surfaces using a sequence of images from a stereoscope. We introduce a warping field based on the Embedded Deformation (ED) nodes with 3D shapes recovered from consecutive pairs of stereo images. The warping field is estimated by deforming the last updated model to the current live model. Our SLAM system can: (1) Incrementally build a live model by progressively fusing new observations with vivid accurate texture. (2) Estimate the deformed shape of unobserved region with the principle As-Rigid-As-Possible. (3) Show the consecutive shape of models.(4) Estimate the current relative pose between the soft-tissue and the scope. In-vivo experiments with publicly available datasets demonstrate that the 3D models can be incrementally built for different soft-tissues with different deformations from sequences of stereo images obtained by laparoscopes. Results show the potential clinical application of our SLAM system for providing surgeon useful shape and texture information in minimal invasive surgery.
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