An Iterative Closest Point Framework for Ultrasound Calibration

2015 ◽  
pp. 69-79 ◽  
Author(s):  
Elvis C. S. Chen ◽  
A. Jonathan McLeod ◽  
John S. H. Baxter ◽  
Terry M. Peters
Keyword(s):  
Iterative Closest Point

scholarly journals Estimating intrafraction tumor motion during fiducial-based liver stereotactic radiotherapy via an iterative closest point (ICP) algorithm

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Wu-zhou Li ◽  
Zhi-wen Liang ◽  
Yi Cao ◽  
Ting-ting Cao ◽  
Hong Quan ◽  
...  
Keyword(s):  
Stereotactic Radiotherapy ◽  
Imaging System ◽  
Liver Tumors ◽  
Three Dimensional ◽  
Rigid Motion ◽  
Iterative Closest Point ◽  
Tumor Motion ◽  
Tracking Accuracy ◽  
Icp Algorithm ◽  
Anterior Posterior

Abstract Background Tumor motion may compromise the accuracy of liver stereotactic radiotherapy. In order to carry out a precise planning, estimating liver tumor motion during radiotherapy has received a lot of attention. Previous approach may have difficult to deal with image data corrupted by noise. The iterative closest point (ICP) algorithm is widely used for estimating the rigid registration of three-dimensional point sets when these data were dense or corrupted. In the light of this, our study estimated the three-dimensional (3D) rigid motion of liver tumors during stereotactic liver radiotherapy using reconstructed 3D coordinates of fiducials based on the ICP algorithm. Methods Four hundred ninety-five pairs of orthogonal kilovoltage (KV) images from the CyberKnife stereo imaging system for 12 patients were used in this study. For each pair of images, the 3D coordinates of fiducial markers inside the liver were calculated via geometric derivations. The 3D coordinates were used to calculate the real-time translational and rotational motion of liver tumors around three axes via an ICP algorithm. The residual error was also investigated both with and without rotational correction. Results The translational shifts of liver tumors in left-right (LR), anterior-posterior (AP),and superior-inferior (SI) directions were 2.92 ± 1.98 mm, 5.54 ± 3.12 mm, and 16.22 ± 5.86 mm, respectively; the rotational angles in left-right (LR), anterior-posterior (AP), and superior-inferior (SI) directions were 3.95° ± 3.08°, 4.93° ± 2.90°, and 4.09° ± 1.99°, respectively. Rotational correction decreased 3D fiducial displacement from 1.19 ± 0.35 mm to 0.65 ± 0.24 mm (P<0.001). Conclusions The maximum translational movement occurred in the SI direction. Rotational correction decreased fiducial displacements and increased tumor tracking accuracy.

Iterative closest point for accurate plane detection in unorganized point clouds

2021 ◽  
Vol 125 ◽  
pp. 103610 ◽  
Author(s):  
Cedrique Fotsing ◽  
Nareph Menadjou ◽  
Christophe Bobda
Keyword(s):  
Point Clouds ◽  
Iterative Closest Point

scholarly journals OICP: An Online Fast Registration Algorithm Based on Rigid Translation Applied to Wire Arc Additive Manufacturing of Mold Repair

Materials ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1563
Author(s):  
Ruibing Wu ◽  
Ziping Yu ◽  
Donghong Ding ◽  
Qinghua Lu ◽  
Zengxi Pan ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Point Cloud ◽  
Point Clouds ◽  
Iterative Closest Point ◽  
Multiple Point ◽  
Icp Algorithm ◽  
Registration Algorithm ◽  
Cloud Models ◽  
Comparison Results ◽  
Wire Arc Additive Manufacturing

As promising technology with low requirements and high depositing efficiency, Wire Arc Additive Manufacturing (WAAM) can significantly reduce the repair cost and improve the formation quality of molds. To further improve the accuracy of WAAM in repairing molds, the point cloud model that expresses the spatial distribution and surface characteristics of the mold is proposed. Since the mold has a large size, it is necessary to be scanned multiple times, resulting in multiple point cloud models. The point cloud registration, such as the Iterative Closest Point (ICP) algorithm, then plays the role of merging multiple point cloud models to reconstruct a complete data model. However, using the ICP algorithm to merge large point clouds with a low-overlap area is inefficient, time-consuming, and unsatisfactory. Therefore, this paper provides the improved Offset Iterative Closest Point (OICP) algorithm, which is an online fast registration algorithm suitable for intelligent WAAM mold repair technology. The practicality and reliability of the algorithm are illustrated by the comparison results with the standard ICP algorithm and the three-coordinate measuring instrument in the Experimental Setup Section. The results are that the OICP algorithm is feasible for registrations with low overlap rates. For an overlap rate lower than 60% in our experiments, the traditional ICP algorithm failed, while the Root Mean Square (RMS) error reached 0.1 mm, and the rotation error was within 0.5 degrees, indicating the improvement of the proposed OICP algorithm.

Robust iterative closest point algorithm with bounded rotation angle for 2D registration

2016 ◽  
Vol 195 ◽  
pp. 172-180 ◽  
Author(s):  
Chunjia Zhang ◽  
Shaoyi Du ◽  
Juan Liu ◽  
Yongxin Li ◽  
Jianru Xue ◽  
...  
Keyword(s):  
Rotation Angle ◽  
Iterative Closest Point ◽  
Iterative Closest Point Algorithm

Robust Euclidean alignment of 3D point sets: the trimmed iterative closest point algorithm

2005 ◽  
Vol 23 (3) ◽  
pp. 299-309 ◽  
Author(s):  
Dmitry Chetverikov ◽  
Dmitry Stepanov ◽  
Pavel Krsek
Keyword(s):  
Iterative Closest Point ◽  
Point Sets ◽  
Iterative Closest Point Algorithm

scholarly journals ITERATIVE CLOSEST POINT ALGORITHM FOR ACCURATE REGISTRATION OF COARSELY REGISTERED POINT CLOUDS WITH CITYGML MODELS

2019 ◽  
Vol IV-2/W5 ◽  
pp. 201-208 ◽  
Author(s):  
S. Goebbels ◽  
R. Pohle-Fröhlich ◽  
P. Pricken
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Iteration Step ◽  
Iterative Closest Point ◽  
Target Point ◽  
Standard Tool ◽  
Iterative Closest Point Algorithm ◽  
Test Scenarios ◽  
Plane Mode ◽  
City Models

<p><strong>Abstract.</strong> The Iterative Closest Point algorithm (ICP) is a standard tool for registration of a source to a target point cloud. In this paper, ICP in point-to-plane mode is adopted to city models that are defined in CityGML. With this new point-to-model version of the algorithm, a coarsely registered photogrammetric point cloud can be matched with buildings’ polygons to provide, e.g., a basis for automated 3D facade modeling. In each iteration step, source points are projected to these polygons to find correspondences. Then an optimization problem is solved to find an affine transformation that maps source points to their correspondences as close as possible. Whereas standard ICP variants do not perform scaling, our algorithm is capable of isotropic scaling. This is necessary because photogrammetric point clouds obtained by the structure from motion algorithm typically are scaled randomly. Two test scenarios indicate that the presented algorithm is faster than ICP in point-to-plane mode on sampled city models.</p>

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