Improved Iterative Closest Point Algorithm Research Articles

An accurate scapula registration process in shoulder arthroplasty using mixed reality.

Arthroplasty surgery can be challenging because of limited exposure of the bones. However, in shoulder arthroplasty for example, a good positioning of the glenoid component is essential to mitigate risks of revision surgeries. To improve the procedure's outcomes, mixed reality can be used as a guidance system relying on a fundamental registration step between the patient's bone and its corresponding 3D model. We present a complete registration workflow for shoulder arthroplasty using Hololens 2 Head Mounted Display. We rely on acquisitions made thanks to our marker-based tracking system, an improved Iterative Closest Point algorithm and verification steps. Our accuracy targets are 1.5 ± 1.5mm for the glenoid guidewire entry point on both antero-posterior and supero-inferior axes, and 1.5 ± 1[Formula: see text] for inclination and version. The overall process must last less than 5min. We have evaluated our process on a cohort of 13 3D printed glenoid bones of all types, showing an average accuracy of 0.84 ± 0.58mm on the antero-posterior axis and 0.49 ± 0.41mm on the supero-inferior one for the entry point. As for inclination and version, we have 0.89±0.6[Formula: see text] and 0.97±0.8[Formula: see text], respectively. The mean process time is about 1min 24s. We have developed a complete, embedded registration workflow and a verification protocol to evaluate our accuracy. Our results are promising for the improvement of the glenoid guidewire placement. Moreover, everything is performed in the field of view of the surgeon, which allows them to fully concentrate on the surgical site.

Robust Map Merging Method for Collaborative LiDAR-based SLAM Using GPS Sensor

Typical lidar-based mappings need to cover the entire environment alone, which often leads to long build cycles and large computational resource requirements. Collaborative simultaneous localization and mapping have been applied to explore large and complex scenarios. However, merging local maps from multiple robots remains a challenge. It is crucial for Collaborative SLAM to identify overlapping areas and obtain the transformation matrix for map merging. To this end, this paper proposes a map merging method for collaborative lidar-based SLAM based on GPS measurements and improved Iterative Closest Point (ICP). We add a GPS sensor to the traditional robot configuration and use a simplified model to convert latitude and longitude data into plane coordinate data. According to Euclidean geometry, we determine the overlapping regions scanned by robots and calculate the initial pose transformation matrix between different robots. Based on the traditional ICP algorithm, KD-tree and normal vectors are used to register point clouds in overlapping regions accurately. The transformation matrix obtained by precise registration is used to rotate and translate the source local point cloud, so as to obtain the global point cloud map. In the real-world experiment, the initial transformation matrix is successfully obtained based on GPS measurements, which provides a good initial pose for the ICP algorithm. Compared with the ICP algorithm, the time consumption and RMSE of the improved ICP algorithm are reduced by 26.3% and 7.94%, respectively. Experimental results demonstrate that the proposed method can achieve accurate and efficient map fusion for multi-robot collaborative SLAM.

Research on point cloud registration of industrial parts based on FGR-ICP algorithm

In the industrial application environment, there are some challenges to point cloud registration caused by disordered and occluded industrial parts. A point cloud registration method combining fast global alignment (FGR) and improved ICP is proposed for the problem that the traditional Iterative Closest Point (ICP) needs to rely on good initialization quality and easily falls into local optimal solutions. The method can optimize the different components of the objective function alternately by FGR to obtain the optimal initial transformation matrix. An improved ICP algorithm was used for exact matching of the initial transformation results. The experimental results showed that the method in this paper had greatly improved the matching accuracy, and the speed had been improved by an order of magnitude compared with the traditional method.

A Fast Point Clouds Registration Algorithm for Laser Scanners

Point clouds registration is an important step for laser scanner data processing, and there have been numerous methods. However, the existing methods often suffer from low accuracy and low speed when registering large point clouds. To meet this challenge, an improved iterative closest point (ICP) algorithm combining random sample consensus (RANSAC) algorithm, intrinsic shape signatures (ISS), and 3D shape context (3DSC) is proposed. The proposed method firstly uses voxel grid filter for down-sampling. Next, the feature points are extracted by the ISS algorithm and described by the 3DSC. Afterwards, the ISS-3DSC features are used for rough registration with the RANSAC algorithm. Finally, the ICP algorithm is used for accurate registration. The experimental results show that the proposed algorithm has faster registration speed than the compared algorithms, while maintaining high registration accuracy.

Point cloud registration algorithm based on curvature feature similarity

In this paper, an improved iterative closest point (ICP) algorithm based on the curvature feature similarity of the point cloud is proposed to improve the performance of classic ICP algorithm in an unstructured environment, such as alignment accuracy, robustness and stability. A K-D tree is introduced to segment the 3D point cloud for speeding up the search of nearest neighbor points using principal component analysis for coarse alignment based on the classical ICP algorithm. In the curvature calculation process, discrete index mapping and templates are taken for sampling to simplify the point cloud data and improve the registration efficiency. The nearest point of Euclidean distance is searched based on curvature feature similarity to improve alignment accuracy. The results of the point cloud alignment experiments show that the response time of the algorithm is less than 5s in the case where the curvature features of point cloud are obvious. And the alignment error can be reduced to 1% of the pre-alignment error. Therefore, the algorithm is an efficient, accurate and practical option for unstructured environment applications.

A compensation method for the eccentricity and inclination errors of spiral bevel gear based on improved ICP algorithm

Due to the manufacturing and assembly errors of the spiral bevel gear, the deviations between the measured tooth surface and its theoretical tooth surface will affect the measurement results of the tooth surface. After analyzing the tooth surface deviation of spiral bevel gear, this paper provided a compensation method to compensate the eccentricity and inclination errors by the registration of the theoretical and measured tooth surface. An improved iterative closest point (ICP) algorithm for the registration of the tooth surface was provided. The distance between the point in measured tooth surface and the nearest point in theoretical tooth surface is used to replace the minimum Euclidean distance, and the damping Gauss-Newton method is used to solve the geometric transformation matrix. Simulation experiments were carried out to verify the proposed compensation method. The compensation effect is over 93% for the concave tooth surface and over 89% for the convex tooth surface of the spiral bevel gear. The results also show that the improved ICP algorithm could compensate the eccentricity and inclination errors of the spiral bevel gear more precisely than the basic ICP algorithm.

Application of the Terrestrial Laser Scanning in Slope Deformation Monitoring: Taking a Highway Slope as an Example

Slope deformation monitoring is the prerequisite for disaster risk assessment and engineering control. Terrestrial laser scanning (TLS) is highly applicable to this field. Coarse registration method of point cloud based on scale-invariant feature transform (SIFT) feature points and fine registration method based on the k-dimensional tree (K-D tree) improved iterative closest point (ICP) algorithm were proposed. The results show that they were superior to other algorithms (such as speeded-up robust features (SURF) feature points, Harris feature points, and Levenberg-Marquardt (LM) improved ICP algorithm) when taking the Stanford Bunny as an example, and had high applicability in coarse and fine registration. In order to integrate the advantages of point measurement and surface measurement, an improved point cloud comparison method was proposed and the optimal model parameters were determined through model tests. A case study was conducted on the left side of the K146 + 150 point at S236 Boshan section, Shandong Province, and research results show that from 14 August 2018 and 9 November 2019, the overall deformation of the slope was small with a maximum value of 0.183 m, and the slope will continue to maintain a stable state without special inducing factors such as earthquake, heavy rainfall and artificial excavation.

3D scene reconstruction based on improved ICP algorithm

3D scene reconstruction is an essential process in many computer vision fields, such as multi-robot mapping and localization. When robots capture frames in different place, which entails estimating the transformation between the sequence of frame. These issues make the 3D scene reconstruction more complication. In order to overcome such difficulty. An improved approach to 3D scene reconstruction based on fast point feature histograms (FPFH) and iterative closest point (ICP) method was proposed in this paper. First, by changing the weight calculation formula, using matched improved FPFH descriptors extracted from depth images as the initial alignment estimate can provide more robust and accurate position compared with conventional matching method. Second, the best-bin-first (BBF) is used to reduce the data dimension, which greatly accelerates the ICP iteration speed of massive point cloud data. The experiments results show that the method can quickly and effectively create complex color point cloud models of recorded scenes or objects, and has high practical value.

BASED ON GPU FOR STRIP ADJUSTMENT ALGORITHM OF LIDAR DATA

Abstract. In order to solve the problem that the source of LiDAR data error needs to be adjusted and the data volume is large, the adjustment speed between the voyages is slow and cannot be automatically adjusted. Based on the iterative nearest point (ICP) algorithm, this paper proposes an improved iterative closest point (ICP) algorithm based on GPU parallel octree. The algorithm quickly constructs the octree of LiDAR nautical belt data in the GPU, uses the octree to quickly find the overlapping area of the nautical band, and then uses the ICP algorithm in the overlapping area to solve the adjustment parameters R and T quickly. Then the entire flight belt is quickly adjusted. Experiments with example data show that this method can quickly and automatically adjustment a large number of LiDAR data, and the adjustment precision can meet the precision requirements of the production.

Improved Iterative Closest Point(ICP) 3D point cloud registration algorithm based on point cloud filtering and adaptive fireworks for coarse registration

ABSTRACTAiming at the problem of long computation time and poor registration accuracy in the current three-dimensional point cloud registration problem, this paper presents a k-dimensional Tree(KD-tree) improved ICP algorithm(KD-tree_ICP) that combines point cloud filtering and adaptive fireworks algorithms for coarse registration. On the basis of the typical KD-tree improved ICP algorithm, the point cloud filtering process and adaptive firework coarse registration process are added. Firstly, the point cloud data acquired by the 3D laser scanner is filtered. And then the adaptive fireworks algorithm is used to perform coarse registration on the filtered point cloud data. Next, the KD-tree_ICP algorithm is used to perform accurate registration on the basis of coarse registration, and the obtained translation and rotation relations are applied to the original point cloud data to obtain the result after registration. Finally, 3D point clouds of physical models of five statues are used for experimental verification, including error analysis, stability analysis and comparison with other algorithms. The experimental results show that the method proposed in this paper has greatly improved the calculation speed and accuracy, and the algorithm is stable and reliable, which can also be applied to the reconstruction of 3D building models, restoration of cultural relics, precision machining and other fields.

A fast registration algorithm of rock point cloud based on spherical projection and feature extraction

Point cloud registration is an essential step in the process of 3D reconstruction. In this paper, a fast registration algorithm of rock mass point cloud is proposed based on the improved iterative closest point (ICP) algorithm. In our proposed algorithm, the point cloud data of single station scanner is transformed into digital images by spherical polar coordinates, then image features are extracted and edge points are removed, the features used in this algorithm is scale-invariant feature transform (SIFT). By analyzing the corresponding relationship between digital images and 3D points, the 3D feature points are extracted, from which we can search for the two-way correspondence as candidates. After the false matches are eliminated by the exhaustive search method based on random sampling, the transformation is computed via the Levenberg-Marquardt-Iterative Closest Point (LM-ICP) algorithm. Experiments on real data of rock mass show that the proposed algorithm has the similar accuracy and better registration efficiency compared with the ICP algorithm and other algorithms.

Three-Dimensional Point Cloud Registration Based on Normal Vector Angle

The widespread use of three-dimensional laser scanning makes point cloud registration essential for model reconstruction. Although iterative closest point (ICP) is a widely used algorithm for automatic and accurate point cloud registration, the ICP algorithm is time-intensive and typically falls in local optima. We employ the normal vector angle of the 3D model and integrate a heuristic search into the ICP algorithm to improve its efficiency, especially while registering complex surfaces. We compare this improved ICP algorithm with the original ICP algorithm and variants of the algorithm in terms of convergence, robustness, and response from a poor initial cloud point position to verify that the proposed improvements outperform the conventional ICP algorithm in the three evaluated aspects when processing point clouds with complex surfaces.

Rigid blocks matching method based on contour curves and feature regions

This study proposes a blocks matching method based on contour curves and feature regions that improve the matching precision and speed with which rigid blocks with a specified thickness in point clouds are matched. The method comprises two steps: coarse matching and fine matching. In the coarse matching step, the rigid blocks are first segmented into a series of surfaces and the fracture surfaces are distinguished. Then, the contour curves of the fracture surfaces are extracted using an improved boundary growth method and the rigid blocks are coarsely matched with them. In the fine matching step, feature regions are first extracted from the fracture surfaces. Then, the centroid of each feature region is calculated and the fine matching of rigid blocks with the centroid sets is completed using an improved iterative closest point (ICP) algorithm. The improved ICP algorithm integrates the rotation angle constraint and dynamic iteration coefficient into a probability ICP algorithm, which significantly improves matching precision and speed. Experiments conducted using public blocks and Terracotta Warriors blocks indicate that the proposed method carries out rigid blocks matching more accurately and rapidly than various conventional methods.

一种聚类改进的迭代最近点配准算法

一种聚类改进的迭代最近点配准算法

Registration of partially overlapping laser-radar range images

To register partially overlapping three-dimensional point sets from different viewpoints, it is necessary to remove spurious corresponding point pairs that are not located in overlapping regions. Most variants of the iterative closest point (ICP) algorithm require users to manually select the rejection parameters for discarding spurious point pairs between the registering views. This requirement often results in unreliable and inaccurate registration. To overcome this problem, we present an improved ICP algorithm that can automatically determine the rejection percentage to reliably and accurately align partially overlapping laser-radar (ladar) range images. The similarity of k neighboring features of each nonplanar point is employed to determine reasonable point pairs in nonplanar regions, and the distance measurement method is used to find reasonable point pairs in planar regions. The rejection percentage can be obtained from these two sets of reasonable pairs. The performance of our algorithm is compared with that of five other algorithms using various models with low and high curvatures. The experimental results show that our algorithm is more accurate and robust than the other algorithms. © 2015

Registration of the Cone Beam CT and Blue-Ray Scanned Dental Model Based on the Improved ICP Algorithm

Multimodality image registration and fusion has complementary significance for guiding dental implant surgery. As the needs of the different resolution image registration, we develop an improved Iterative Closest Point (ICP) algorithm that focuses on the registration of Cone Beam Computed Tomography (CT) image and high-resolution Blue-light scanner image. The proposed algorithm includes two major phases, coarse and precise registration. Firstly, for reducing the matching interference of human subjective factors, we extract feature points based on curvature characteristics and use the improved three point's translational transformation method to realize coarse registration. Then, the feature point set and reference point set, obtained by the initial registered transformation, are processed in the precise registration step. Even with the unsatisfactory initial values, this two steps registration method can guarantee the global convergence and the convergence precision. Experimental results demonstrate that the method has successfully realized the registration of the Cone Beam CT dental model and the blue-ray scanner model with higher accuracy. So the method could provide researching foundation for the relevant software development in terms of the registration of multi-modality medical data.