China
Africa
Explore chapters and articles related to this topic
A History of Surface Guidance Methods in Radiation Therapy
Published in Jeremy D. P. Hoisak, Adam B. Paxton, Benjamin Waghorn, Todd Pawlicki, Surface Guided Radiation Therapy, 2020
Jeremy D. P. Hoisak, Todd Pawlicki
The primary objective of surface imaging in RT is to compare the current or “live” patient surface to a reference surface and compute the current displacements required to bring the two surfaces into alignment. Several computational approaches to the problem of registering two surfaces have been proposed and implemented; however, the commonly employed method in SGRT systems is based on the ICP algorithm. The ICP algorithm establishes a correspondence between the closest points in the point clouds of their respective surfaces. The root mean square of the estimated translations and rotations required to match the corresponding points is then minimized. The transformation is applied, and new point correspondences are established. The algorithm continues to iterate until a global minimum in the solution space is found.
Perception
Published in Hanky Sjafrie, Introduction to Self-Driving Vehicle Technology, 2019
The most cited scan matching technique is undoubtedly the Iterative Closest Point (ICP), originally introduced by Besl and McKay [5] and later adapted by Lu and Milios for localization applications [40]. ICP is an iterative algorithm that aims to minimize the point-to-point distances between two scans. The algorithm consists of three major steps: For each point in the reference (or first) scan, find the correspondence by selecting the closest point or the nearest neighbor in the object (or second) scan.Calculate the rigid body transformation that minimizes the mean square error of all correspondence pairs of the reference and the object scan.Apply the transformation to the object scan and repeat until you achieve convergence.
Strip Adjustment
Published in Jie Shan, Charles K. Toth, Topographic Laser Ranging and Scanning, 2018
Charles K. Toth, Zoltan Koppanyi
The most popular point cloud matching, the ICP algorithm, is commonly used for navigation in computer vision and robotics, and for point cloud coregistration in terrestrial laser scanning applications. The classical ICP (Besl and McKay, 1992; Madhavan et al., 2005) attempts to find the optimal transformation between two point clouds by minimizing the average distance of corresponding point pairs: () minT1N(Q)∑N(Q)i=1〈match(qi,P,〈.,.〉),T(qi)〉
Grid graph-based large-scale point clouds registration
Published in International Journal of Digital Earth, 2023
Yi Han, Guangyun Zhang, Rongting Zhang
Points-based registration uses mathematical rules instead of feature descriptor to identify corresponding pairs. There are some point-based methods in the literature which do not follow the paradigm of feature descriptors calculation and feature matching. For example, iterative closest point (ICP) (Besl and McKay 1992) is the most extensively used point cloud registration approach. ICP heuristically chooses the closest point in Euclidean space as a correspondence; then, a rigid transformation is fitted by using these correspondences. Thus, ICP searches for local optimum of initial transformation by alternating between finding the correspondences and computing the best transformation given these correspondences. Many variants, such as point-to-plane, plane-to-plane and normal, are proposed to improve one or more constraints of ICP. ICP is still sensitive to partial overlaps, and ICP will almost always fail in 3D scan sequences with partial overlaps. Many methods introduce robust transformation estimation technique to improve the robustness of ICP, such as Trimmed ICP (Chetverikov, Stepanov, and Krsek 2005), and Go-ICP (Yang, Li et al. 2016). ICP and its variant high relies on the initial transformation parameter.
Comparative analysis of mobile laser scanning and terrestrial laser scanning for the indoor mapping
Published in Building Research & Information, 2023
Abdurahman Yasin Yiğit, Seda Nur Gamze Hamal, Ali Ulvi, Murat Yakar
The second part, the ICP algorithm, aims to find the transformation between a point cloud and some reference surface (or another point cloud) by minimizing the frame errors between the corresponding entities. At this stage, the ‘local-map’ settings are also adjusted. The geometric criteria of the 3D point cloud map to be created here have been determined. After the odometer, the ‘create map’ section was started. Here, it divides the entire trajectory into a set of maps, creating a series of maps. The created maps are then used to align the model. After the create map, the global optimization part was started. In this step, the fragmented maps are aligned. In the parameter setting in this section, the default value of seventy ICP iterations is selected. The main purpose of this section is to increase the accuracy. After parameter setting, the fragmented maps are connected by throwing tie.
A 3D registration methodology to evaluate the goodness of fit at the individual-respiratory mask interface
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2021
J. W. R. Verberne, P. R. Worsley, D. L. Bader
To review briefly, both the face and the mask models were loaded into Ampscan with the.stl files. The following steps were then taken to align and register the mask onto the face; (i) An initial rough alignment was performed using four points that were manually selected on the face (nasal bridge, cheeks and chin), corresponding to key reference points on the mask. (ii) An Iterative Closest Point (ICP) algorithm was used to align the points to each other. Any of the face vertices that were at a distance of more than 20 mm from the mask were automatically removed to reduce the effective computational time. (iii) An ICP algorithm was subsequently run to align the face to the entire mask interface. This algorithm was designed to minimize the mean absolute distance between all mask vertices and their nearest neighbour on the face. The mask was translated and rotated in each plane to achieve the closest fit. (iv) A registration shape was generated depicting the distance between each mask vertex and its nearest neighbour on the face, using a colour contour plot. If the mask was displaced into the face, the distance value was multiplied by −1.