郭文康, 梅剑东, 孙荣川, 郁树梅, 孙立宁. 基于三角剖分的内窥镜肠道手术机器人体素地图构建方法[J]. 机器人, 2021, 43(4): 395-405.DOI: 10.13973/j.cnki.robot.200453.
GUO Wenkang, MEI Jiandong, SUN Rongchuan, YU Shumei, SUN Lining. A Voxel Map Construction Method of Endoscopic Intestinal Surgery RobotBased on Triangulation. ROBOT, 2021, 43(4): 395-405. DOI: 10.13973/j.cnki.robot.200453.
Abstract：Aiming at a fully autonomous robotic system for endoscopic diagnosis and treatment, a method for constructing a voxel map of the internal structure of the human intestine is proposed to realize accurate estimation of the endoscope pose and construct a voxel map for the navigation of the surgical robot. A monocular endoscope is used to capture image sequences of the intestinal interior in this method. Firstly, the endoscopic trajectory is estimated based on a monocular SLAM (simultaneous localization and mapping) method and a sparse map of the internal structure of the intestinal tract is constructed. Based on the sparse feature point map, a densification solution of sparse map based on triangulation is proposed to construct a voxel map with dense geometric information for path planning of surgical instruments. In experimental study, voxel maps are constructed in the simulated intestine and the in vitro pig intestine respectively, and the error evaluation is carried out. The mean square errors of the diameter and lesion size in the simulated intestine are 1.16 mm and 0.81 mm, respectively. The mean square error of endoscopic positioning is 2.163 mm.
[1] Sun D Y, Liu J Q, Linte C-A, et al.Surface reconstruction from tracked endoscopic video using the structure from motion approach[C]//6th International Workshop on Medical Imaging and Augmented Reality / 8th International Workshop on Augmented Reality for Computer-Assisted Interventions. Berlin, Germany: Springer, 2013: 127-135. [2] Collins T, Bartoli A.Towards live monocular 3D laparoscopy using shading and specularity[C]//3rd International Conference on Information Processing in Computer-Assisted Interventions. Berlin, Germany: Springer, 2012: 11-21. [3] Hu M, Penney G, Figl M, et al.Reconstruction of a 3D surface from video that is robust to missing data and outliers: Application to minimally invasive surgery using stereo and mono endoscopes[J]. Medical Image Analysis, 2012, 16(3): 597-611. [4] Davison A J. Real-time simultaneous localisation and mapping with a single camera[C]//9th IEEE International Conference on Computer Vision. Piscataway, USA: IEEE, 2003: 1403-1410. [5] Kitt B, Geiger A, Lategahn H.Visual odometry based on stereo image sequences with RANSAC-based outlier rejection scheme [C]//IEEE Intelligent Vehicles Symposium. Piscataway, USA: IEEE, 2010: 486-492. [6] Henry P, Krainin M, Herbst E, et al.RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments[J]. International Journal of Robotics Research, 2012, 31(5): 647-663. [7] Mur-Artal R, Montiel J M M, Tardos J D.ORB-SLAM: A versatile and accurate monocular SLAM system[J]. IEEE Transactions on Robotics, 2015, 31(5): 1147-1163. [8] Mur-Artal R, Tardos J D.ORB-SLAM2: An open-source SLAM system for monocular, stereo, and RGB-D cameras[J]. IEEE Transactions on Robotics, 2017, 33(5): 1255-1262. [9] Xie C J, Yao T L, Wang J, et al.Endoscope localization and gastrointestinal feature map construction based on monocular SLAM technology[J]. Journal of Infection and Public Health, 2020, 13(9): 1314-1321. [10] Mahmoud N, Cirauqui I, Hostettler A, et al.ORBSLAM-based endoscope tracking and 3D reconstruction[C]//International Workshop on Computer-Assisted and Robotic Endoscopy. Cham, Switzerland: Springer, 2017: 72-83. [11] Chen L, Tang W, John N W, et al.SLAM-based dense surface reconstruction in monocular minimally invasive surgery and its application to augmented reality[J]. Computer Methods and Programs in Biomedicine, 2018, 158: 135-146. [12] Engel J, Thomas S, Cremers D.LSD-SLAM: Large-scale direct monocular SLAM[C]//13th European Conference on Computer Vision. Berlin, Germany: Springer, 2014: 834-849. [13] Hornung A, Wurm K M, Bennewitz M, et al.OctoMap: An efficient probabilistic 3D mapping framework based on octrees[J]. Autonomous Robots, 2013, 34(3): 189-206. [14] Almario C V, Chey W D, Iriana S, et al.Computer versus physician identification of gastrointestinal alarm features[J]. International Journal of Medical Informatics, 2015, 84(12): 1111-1117. [15] Rublee E, Rabaud V, Konolige K, et al.ORB: An efficient alternative to SIFT or SURF[C]//International Conference on Computer Vision. Piscataway, USA: IEEE, 2011: 2564-2571. [16] Lowe D G.Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91-110. [17] Bay H, Tuytelaars T, van Gool L.SURF: Speeded up robust features[C]//European Conference on Computer Vision. Berlin, Germany: Springer, 2006: 404-417. [18] Lo S H.A new mesh generation scheme for arbitrary planar domains[J]. International Journal for Numerical Methods in Engineering, 1985, 21(8): 1403-1426. [19] 刘强强．基于OcTree的SLAM系统地图算法的研究[D]．西安:西安电子科技大学, 2015. Liu Q Q. A study of SLAM system algorithm based on OcTree[D]. Xi'an: Xidian University, 2015. [20] Rusu R B, Cousins S.3D is here: Point Cloud Library (PCL) [C]//IEEE International Conference on Robotics and Automation. Piscataway, USA: IEEE, 2011: 1-4. [21] 刘利, 郑小林, 张绍祥, 等．数字人体肠道的三维可视化研究[J]．第三军医大学学报, 2008, 30(6):481-484. Liu L, Zheng X L, Zhang S X, et al. Visualization of intestinal canal of the Chinese visible human[J]. Acta Academiae Medicinae Militaris Tertiae, 2008, 30(6): 481-484.
