A three-dimensional approach to visualize pairwise morphological variation and its application to fragmentary palaeontological specimens

The iterative closest point (ICP) algorithm is widely used in three-dimensional (3D) point cloud registration, and it is very stable and robust. However, its biggest drawback is being easily trapped in a local optimal solution, which results in the incorrect registration result. Currently, there is neither a clear effective range to define whether the ICP algorithm will fall into a local optimum nor a study providing a comprehensive evaluation of the ICP algorithm. In this paper, we take the overlap ratio, angle, distance, and noise as the influencing factors of ICP and evaluate the validity, robustness, accuracy, and efficiency of point-to-point and point-to-plane ICP by using four datasets. We first analyze the effective ranges of the two ICP algorithms with respect to overlap ratio, angle, and distance and then propose a universal effective range for the three factors. Next, the effect of Gaussian noise on the validity and accuracy of two ICPs is evaluated. We also analyze the factors influencing ICP accuracy and explain their changing rules. We finally study the effect of different parameters on the efficiency. All results are compared by using point-to-point and point-to-plane ICP algorithms. The results show that the overlap ratio has no effect on validity, but it has a significant influence on accuracy. The angle has a great impact on the validity and efficiency of ICP but has no effect on accuracy. The distance only affects validity, has a limited effect on efficiency and no effect on accuracy. Meanwhile, Gaussian noise has a little effect on the validity. In addition, the general effective range of point-to-point ICP is larger than that of point-to-plane ICP, but the point-to-plane ICP algorithm presents a better efficiency. The point-to-point ICP is more robust to Gaussian noise with respect to validity, while the point-to-plane is more resistant in terms of accuracy.

A bounded iterative closest point method for minimally invasive registration of the femur

Three-dimensional Feature Recognition-based Automated Patient Treatment Mismatch Verification System for Radiation Therapy

Point set registration algorithms such as Iterative Closest Point (ICP) are commonly utilized in time-constrained environments like robotics. Finding the nearest neighbor of a point in a reference 3D point set is a common operation in ICP and frequently consumes at least 90% of the computation time. We introduce a novel approach to performing the distance-based nearest neighbor step based on Delaunay triangulation. This greedy algorithm finds the nearest neighbor of a query point by traversing the edges of the Delaunay triangulation created from a reference 3D point set. Our work integrates the Delaunay traversal into the correspondences search of ICP and exploits the iterative aspect of ICP by caching previous correspondences to expedite each iteration. An algorithmic analysis and comparison is conducted showing an order of magnitude speedup for both serial and vector processor implementation.

Point set augmentation through fitting for enhanced ICP registration of point clouds in multisensor coordinate metrology

The objective of this study is to compare landmark-based assessment of hominin mandible morphological variation with that from a high-density point cloud, using 3D models constructed from structure-from-motion (SfM) surface capture techniques. Surface models of nine hominin mandible casts were created using SfM photogrammetry. The morphology of these models was described using traditional geometric morphometrics based on identification of landmarks. This was compared to the morphological variation described by the differences between high-density point clouds, which do not rely on anatomical landmarks, using an iterative closest point algorithm. The landmark-based approach grouped the anatomically modern human and Neanderthal mandibles with reasonable success. The high-density point cloud approach also grouped these successfully, but was able to incorporate information from a specimen that was insufficiently preserved to be included in the landmark data set. This improved the accuracy of the grouping. The use of high-density point clouds from surface capture to analyse hominin mandible morphology allows for greater amounts of information to be included and offers a potential method to identify shape affinity that is as successful as landmark-based geometric morphometrics.

Differential LiDAR (Light Detection and Ranging) from repeated surveys has recently emerged as an effective tool to measure three-dimensional (3D) change for applications, such as quantifying slip and spatially distributed warping associated with earthquake ruptures, and examining the spatial distribution of beach erosion after hurricane impact. Currently, the primary method for determining 3D change from LiDAR is through the use of the iterative closest point (ICP) algorithm and its variants. However, all current studies using ICP have assumed that all LiDAR points in the compared point clouds have uniform accuracy. This assumption is simplistic given that the error for each LiDAR point is variable, and dependent upon time varying factors such as target range, angle of incidence, and aircraft trajectory accuracy. Therefore, to rigorously determine spatial change, it would be ideal to model the random error for every LiDAR observation in the differential point cloud, and use these error estimates as apriori weights in the ICP algorithm. To test this approach, we implemented a rigorous LiDAR observation error propagation method to generate estimated random error for each point in a LiDAR point cloud, and then determine 3D displacements between two point clouds using an anisotropic weighted ICP (A-ICP) algorithm. The algorithm was evaluated by qualitatively and quantitatively comparing point clouds with synthetic fault ruptures between a uniform weight and anistropically weighted ICP algorithm. Then post-earthquake slip is estimated for the 2010 El Mayor-Cucapah Earthquake (EMC), using pre- and post-event LiDAR. Based on the analysis, Moving Window A-ICP is able to better estimate the synthetic surface ruptures, and provides a smoother estimate of actual displacement for the EMC earthquake.

This paper introduces an improved approach for the volume data registration of human retina. Volume data registration refers to calculating out a near-optimal transformation between two volumes with overlapping region and stitching them together. Iterative closest point (ICP) algorithm is a registration method that deals with registration between points. Classical ICP is time consuming and often traps in local minimum when the overlapping region is not big enough. Optical Coherence Tomography (OCT) volume data are several separate, partially overlapping tiles. To stitch them together is a technology in computer aided diagnosis. In this paper, a new 3D registration algorithm based on improved ICP is presented. First, the Canny edge detector is applied to generate the point cloud set of OCT images. After the detection step, an initial registration method based on the feature points of the point cloud is proposed to determine an initial transformation matrix by using singular value decomposition (SVD) method. Then, an improved ICP method is presented to accomplish fine registration. Corresponding point in the point cloud is weighted to reduce the iteration times of ICP algorithm. Finally, M-estimation is used as the objective function to decrease the impact of outliers. This registration algorithm is used to process human retinal OCT volume pairs that contain an overlapping region of 75ź?ź500ź?ź375 voxels approximately. Then a comparative experiment is conducted on some public-available datasets. The experimental results show that the proposed method outperforms the classical method.

This paper presents a method to distinguish between a manipulator and its surroundings using a depth sensor. The depth sensor used is Kinect. First Kinect calibration is addressed. Then coordinate calibration between Kinect and the manipulator are solved using iterative least square (ILS) algorithm. At this point, to delete the robot from the scene and keep only the surrounding surface, the accuracy of homogeneous transformation acquired from ILS is inadequate. We further focus on a matching method between the manipulator's model and point cloud, to use iterative closest point (ICP) algorithm. ICP enhances the accuracy for a great deal. Experiment shows that this comprehensive method is practical and robust. It can be used in dynamic environment as well.

Morphological study of lateral meniscus using statistical shape modelling: a study using data from the osteoarthritis initiative

The iterative closest point (ICP) tends to fall into local optimality due to inaccurate initial poses during the registration of the multi-view 3D cloud. Therefore, this paper proposes a parallel ICP algorithm with conditional constraint corresponding points. To achieve a fine registration of the point cloud, the corresponding point set is first filtered by adding the normal and color information. Then the OpenMP is introduced to accelerate the program in parallel for ICP. To verify the effectiveness of our algorithm, in the V-Rep simulation environment, the multi-view point cloud data of the scene is obtained by the RGB-D cameras from different angles point cloud. The results show that our algorithm can fuse the multi-view point cloud, improve the accuracy and real-time performance of ICP. Furthermore, in a large-scale calculation, the average single iteration time is less than 0.1s, and the RMSE (root mean square error) is about 0.1, which meets the need of target recognition and sorting in a three-dimensional industrial scene.

Robust symmetric iterative closest point

To solve the problem of calculating the maximum linear deviation in block cipher algorithm linear analysis, the paper has proposed a search method and improved algorithm based on iterative closest point (ICP) of the maximum deviation. The ciphertext and its coefficient matrix is initialized as range image matrix, using ICP algorithm to calculate the best match state of adjacent range image, and the linear deviation value of round function transformation of this time was proved to be the maximum linear deviation by applying generalized Walsh spectrum; by proposing the initial rule corresponding distance, determined the initial corresponding relation of iterative algorithm, each round of transformation matrix would be assigned to the next round as the initial transformation matrix which has improved the matching speed of the ICP algorithm; the rationality and effectiveness of this new search algorithm can be proved by comparing with the maximum linear deviation search algorithm put forward by M.Matsui.

Localization and mapping are key requirements for autonomous mobile systems to perform navigation and interaction tasks. Iterative Closest Point (ICP) is widely applied for LiDAR scan-matching in the robotic community. In addition, the standard ICP algorithm only considers geometric information when iteratively searching for the nearest point. However, ICP individually cannot achieve accurate point-cloud registration performance in challenging environments such as dynamic environments and highways. Moreover, the computation of searching for the closest points is an expensive step in the ICP algorithm, which is limited to meet real-time requirements, especially when dealing with large-scale point-cloud data. In this paper, we propose a segment-based scan-matching framework for six degree-of-freedom pose estimation and mapping. The LiDAR generates a large number of ground points when scanning, but many of these points are useless and increase the burden of subsequent processing. To address this problem, we first apply an image-based ground-point extraction method to filter out noise and ground points. The point cloud after removing the ground points is then segmented into disjoint sets. After this step, a standard point-to-point ICP is applied into to calculate the six degree-of-freedom transformation between consecutive scans. Furthermore, once closed loops are detected in the environment, a 6D graph-optimization algorithm for global relaxation (6D simultaneous localization and mapping (SLAM)) is employed. Experiments based on publicly available KITTI datasets show that our method requires less runtime while at the same time achieves higher pose estimation accuracy compared with the standard ICP method and its variants.

This paper proposes a new iterative closest point approach based on correntropy with feature guided. Iterative Closest Point (ICP) algorithm can deal with most rigid registration problems, but for point sets with lots of noise and outliers, ICP cannot achieve high precision. We introduce correntropy into ICP to handle this problem by suppressing the influence of the noise and outliers. In terms of point sets contain a large proportion of planes or a curved surface, and have single structure, such as a three-dimensional model of upper jaw, we propose a feature-guided model to solve the oral data registration problem, which uses both the feature and the original data to participate in the registration, but with different weights. Our method mainly deals with the point set registration which has single structure and contains outliers. Experimental results demonstrate that the proposed algorithm is precise and robust.