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A Navigation System of Cerebral Endovascular Surgery Integrating Multiple Space-guiding Trackers

A Navigation System of Cerebral Endovascular Surgery Integra...

引用

30th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society

作者： Lee, Jiann-Der Chien, Ting-An Huang, Chung-Hsien Wu, Chien-Tsai Lee, Shin-Tseng Chang Gung Univ Dept Elect Engn Tao Yuan 333 Taiwan Chang Gung Univ Grad Inst Med Mech Tao Yuan Taiwan Chang Gung Mem Hosp Med Augmented Real Res Ctr Tao Yuan Taiwan

ISBN: (纸本)9781424418145

In this paper, a navigation system combining multiple space-guiding trackers is proposed for cerebral endovascular surgery. This system not only provides 2-D and 3-D visualization of medical images but also integrates an icp-based algorithm to increase the registration accuracy during cerebral endovascular navigation. This tracking system integrates a mechanical digitizer, an optical tracker and an electromagnetic sensor to increase the flexibility in clinical application. An icp-based registration algorithm is proposed to dynamically correct the registration error according to the current observation of the cerebral endovascular. To evaluate the system performance, a plastic phantom with cerebral endovascular structures is used and the experimental results show that the localization error is significantly reduced from 5.21 similar to 6.47 mm to 2.29 similar to 2.87 mm using the proposed scheme.

关键词： Navigation Surgery Tracking System Image Registration icp algorithm

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DEVELOPMENT OF INTEGRATION AND ADJUSTMENT METHOD FOR SEQUENTIAL RANGE IMAGES

DEVELOPMENT OF INTEGRATION AND ADJUSTMENT METHOD FOR SEQUENT...

引用

Indoor-Outdoor Seamless Modelling, Mapping and Navigation

作者： Nagara, K. Fuse, T. Univ Tokyo Dept Civil Engn Bunkyo Ku 7-3-1 Hongo Tokyo 1138656 Japan

ISBN: (纸本)9781629935201

With increasing widespread use of three-dimensional data, the demand for simplified data acquisition is also increasing. The range camera, which is a simplified sensor, can acquire a dense-range image in a single shot;however, its measuring coverage is narrow and its measuring accuracy is limited. The former drawback had be overcome by registering sequential range images. This method, however, assumes that the point cloud is error-free. In this paper, we develop an integration method for sequential range images with error adjustment of the point cloud. The proposed method consists of icp (Iterative Closest Point) algorithm and self-calibration bundle adjustment. The icp algorithm is considered an initial specification for the bundle adjustment. By applying the bundle adjustment, coordinates of the point cloud are modified and the camera poses are updated. Through experimentation on real data, the efficiency of the proposed method has been confirmed.

关键词： 3D data Range image Point cloud icp algorithm Self-calibration bundle adjustment

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An Approach to Effective 3D Reconstruction Based on Point Cloud Merging

An Approach to Effective 3D Reconstruction Based on Point Cl...

引用

IEEE International WIE Conference on Electrical and Computer Engineering (IEEE WIECON-ECE)

作者： Mostafa, Sakib Fahim, Masud An Nur Islam Tasnim, Jarin Khulna Univ Engn & Technol Khulna 9203 Bangladesh

ISBN: (纸本)9781509037452

In recent years due to the update of relevant graphical technologies acquiring of 3D reconstructed images from 2D views is getting attention exponentially. Many researchers have therefore tried to develop robust as well as efficient methods to reconstruct 3D models from images. In this paper, we have proposed a method for reconstructing 3D view from multiple images. This approach is based on images which are being taken by calibrated camera. According to the study first we have generated a 3D view from a pair of images which is applicable for other pairs also. Then we have merged the point clouds which has been achieved form the pairs using famous iterative point cloud algorithm in order to gain the final 3D reconstructed view. We have performed several experiments in order to calculate the consistency of the study. For this we have used several databases mostly achieved from Princeton university computer vision group.

关键词： multiple view SFM icp algorithm point cloud triangulation

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Accurate point cloud registration algorithm based on RANSAC 6

Accurate point cloud registration algorithm based on RANSAC

引用

6th International Conference on Robot Systems and Applications (ICRSA)

作者： Ku, Zongfan Chen, Deng Wuhan Inst Technol Hubei Key Lab Intelligent Robot Wuhan Peoples R China

ISBN: (纸本)9798400708039

Point cloud registration is widely used in robot grasping, aerial mapping, 3D reconstruction and other fields. Aiming at the problems of large pose deviation of point clouds in photographic point clouds, large noise and dense number of point clouds, it is difficult for the traditional iterative closest point (icp) algorithm to achieve automatic registration. In order to solve the above problems, an improved precision point cloud registration method is proposed. The method includes two stages: coarse registration and fine registration, in the coarse registration stage, the point cloud is simplified by the Approximate Intrinsic Voxel Structure (AIVS) algorithm, and the simplified point cloud is initially registered by the improved RANSAC algorithm. Then, on the basis of the initial registration, a fast and accurate algorithm (FR-icp) is used for precise registration. This method solves the problem that icp is sensitive to initial pose. Experiments based on the self-constructed auto parts point cloud dataset show that compared with the icp algorithm, the registration accuracy and time of the proposed method are improved by 95.8% and 67.8%, respectively.

关键词： point cloud registration icp algorithm RANSAC algorithm AIVS algorithm FR-icp algorithm

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Progressive Mesh Object Registration

Progressive Mesh Object Registration

引用

IEEE/SICE International Symposium on System Integration

作者： Boehnke, Kay Otesteanu, Marius Polytehn Univ Timisoara Dept Commun Timisoara 300223 Romania

ISBN: (纸本)9781424438389

Using 3D information delivered by laser range sensor becomes more and more common in industrial systems. Especially object recognition and localization in 3D range data is still a challenging task in robotic vision. We connect the registration technique of the well known Iterative Closest Points algorithm with the hierarchical object representation of Progressive Meshes to find objects in a scene. This approach is integrated in an object localization system for industrial robotic bin picking.

关键词： Automation system Robotic bin picking Virtual Reality simulation object recognition and localization icp algorithm

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Full-surface 3-D reconstruction based on surround structured lighting 7

Full-surface 3-D reconstruction based on surround structured...

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7th International Conference on Optical and Photonic Engineering (icOPEN)

作者： Xu, Hao Yin, Wei Yang, Chen Li, Jiachao Feng, Shijie Tao, Tianyang Chen, Qian Zuo, Chao Nanjing Univ Sci & Technol Smart Computat Imaging SCI Lab Nanjing 210094 Jiangsu Peoples R China Nanjing Univ Sci & Technol Jiangsu Key Lab Spectral Imaging & Intelligent Se Nanjing 210094 Jiangsu Peoples R China

ISBN: (数字)9781510631601

ISBN: (纸本)9781510631601

In a conventional fringe projection profilometry (FPP) consisted of a camera and a projector, just one-sided 3D data of the tested object can be obtained by a single-shot measurement. Therefore, tools such as turntables are commonly used to obtain 360-degree 3D point cloud data of objects. However, this method requires multiple measurements and point cloud registration, which is time consuming and laborious. With the help of two planar mirrors, this paper proposes an improved system that captures fringe images from three different perspectives including one real camera and two virtual cameras. The information of the planar mirrors (i.e., the mirror calibration) is achieved by artificially attaching the featured pattern to the surface of the mirrors. Using the calibration parameters of the planar mirrors, the 3D point cloud data obtained by the virtual cameras can be converted into the real coordinate system, thereby reconstructing the full-surface 3D point cloud data with relative roughness. Finally, an improved icp algorithm is introduced to obtain high-precision 360-degree point cloud data. The experimental results demonstrate that with the help of the mirrors, our system can obtain high-quality full-surface 360-degree profile results of the measured object at high speed.

关键词： Fringe projection profilometry (FPP) mirror calibration icp algorithm

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The Indoor SLAM using Multiple Three Dimension Sensors Integration

The Indoor SLAM using Multiple Three Dimension Sensors Integ...

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5th IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS)

作者： Joochim, Chanin Roth, Hubert Univ Siegen Inst Automat Control Engn Dept Elect Engn & Comp Sci D-57068 Siegen Germany

ISBN: (纸本)9781424448814

The high performance mobile robots nowadays should operate themselves in many applications. One attractive task is to build the map, meanwhile estimate their poses for the locomotion in real time. The Simultaneous localization and mapping (SLAM) technique is once technique which can solve this demand. The selecting sensors are essential factors for generation the high accuracy output. This paper present, the applying a new 3D sensor namely the Photonic Mixer Devices (PMD) provides a real-time capturing of surrounding volume. However the output of PMD is very excellent depth metric, to recognize the complex objects is still faultily because of the low resolution output (64x48 pixels). The 2D high resolution camera inspires to compensate the PMD camera's drawback. The high resolution image output is registered on the 3D volume. The visual input from the 2D camera does not delivers only high resolution texture data on 3D volume but it is also used for object recognition. Finally, the Iterative Closest Point (icp) algorithm is used for the image registration in order to yield the real time 3D data frames registration.

关键词： SLAM mobile robot PMD camera icp algorithm 2D image CCD camera and image registration

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PCA Point cloud registration algorithm based on projection similarity This paper uses an improved PCA algorithm to complete the point cloud registration task 6

PCA Point cloud registration algorithm based on projection s...

引用

6th International Conference on Robot Systems and Applications (ICRSA)

作者： Zhang, Ming Wang, Chunmei Yu, Baocheng Xu, Wenxia Wuhan Inst Technol Sch Comp Sci & Engn Artificial Intelligence Wuhan Peoples R China

ISBN: (纸本)9798400708039

To address the issue of slow speed and susceptibility to local optima in traditional iterative closest point (icp) algorithms, a new algorithm was proposed that combines principal component analysis (PCA) and normal vector angle thresholding to remove error points. Firstly, the PCA method was used to find the principal coordinate system of the point cloud. After aligning the principal vectors in the coordinate system, there may be a situation where the direction of two principal vectors is reversed. For this situation, a method was proposed to correct the direction of the principal vectors by using a statistical matrix of the projected point cloud to calculate their similarity, greatly reducing the time for coarse registration. After obtaining a good initial pose for the point cloud, the overlapping parts of the two point clouds were extracted, and the icp algorithm was improved by combining the normal vector angle of the point cloud for fine registration. Experimental results showed that this method can significantly improve registration efficiency and achieve good results.

关键词： Point cloud registration icp algorithm Principal component analysis Normal vector entropy

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A Matching algorithm and Error Analysis on the Basis of Triangle Mesh

引用

International Conference on Frontiers of Manufacturing Science and Measuring Technology (ICFMM2011)

作者： Zhu, Limin Liu, Chunguo Wang, Shaohui Li, Mingzhe Roll Forging Research Institute Jilin University Changchun 130025 China

ISBN: (纸本)9783037851333

In reverse engineering, in order to analyze the accuracy of formed work-piece, the shape error between the formed work-piece and its CAD model need to be calculated. In this paper, a matching method on the basis of triangle mesh is proposed. This matching method includes three parts: pre-positioning, rough registration and fine registration. Firstly, the maximum projective plane of cloud data is designed to parallel to the xy plane through translation and rotation, and cloud data is translated by a vector which can be obtained with the point with the maximum z coordinate and the origin of coordinates. Secondly, cloud data is gradually rotated 360 degree around z axis confirm the angle making the error minimum. In this way, an initial value of icp algorithm can be obtained, which can avoid the local convergence in the algorithm. Finally, icp algorithm can be applied to calculate the surface error further. After matching, the error between work-piece and the CAD model can be calculated by interpolation based on triangle mesh. The results show that the matching method can obtain the higher matching accuracy.

关键词： matching method triangular element icp algorithm error analysis

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Registration algorithm research for work-pieces based on affine invariant

Registration algorithm research for work-pieces based on aff...

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International Conference on Optical Instruments and Technology (OIT) - Optoelectronic Measurement Technology and Systems

作者： Chen, Meng Yan, Bixi Beijing Informat Sci & Technol Univ Sch Photoelect Informat & Commun Engn Beijing Peoples R China

ISBN: (纸本)9780819488428

Work-pieces registration is a crucial item in flexible manufacture assembly. In order to finish work-pieces registration, a method is proposed based on affine invariant. After the CAD model data and actual measurement data of a work-piece are acquired, the method consists of the following five steps: sampling point clouds data, extracting characteristic four-points set, searching characteristic congruent four-points set, computing transformation congruent matrix and the last, further precise point clouds data registration. Point clouds data of CAD and measuring model are sampled respectively through calculating the curvature of the two sets of data and selecting the obvious curvature points as their reduced characteristic points. Based on this, the characteristic four-points set from the reduced ones of CAD model and the corresponding matching congruent four-points set of the measuring model are extracted according to RANSAC algorithm. The rotation matrix R and translation vector T of any two matching four-points are then calculated through the algorithm of quaternion. After that, the measuring model is rotated and translated and then compared with the CAD model data, the most congruent transformation matrix is selected as the coarse registration result. Furthermore, Iterative Closest Point (icp) algorithm is applied to the congruent transformation to improve registration precision. The experiment shows that the run time of the algorithm is 129.56s and mean-error of point-to-point distance is 0.062mm when accessing measuring model data more than 80000 points. Compared with the traditional curvature registration, the experiment also shows that the algorithm is more efficient and robust when the volume of point clouds data is larger.

关键词： registration point cloud affine invariant RANSAC icp algorithm

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