Ground-based Multi-platform Point Clouds Registration

The permanent terrestrial laser scanning (PLS) system has opened the possibilities for efficient data acquisition with high-temporal and spatial resolution, thus allowing for improved capture and analyses of complex geomorphological changes on the Earth's surface. Accurate georeferencing of generated four-dimensional point clouds (4DPC) from PLS is the prerequisite of the following change analysis. Due to the massive data volume and potential changes between scans, however, efficient, robust, and automatic georeferencing of 4DPC remains challenging, especially in scenarios lacking signalized and reliable targets. This georeferencing procedure can be typically realized by designating a reference epoch and registering all other scans to this epoch. Addressing the challenges in targetless registration of topographic 4DPC, we propose a simple and efficient registration method called Piecewise-ICP, which first segments point clouds into piecewise patches and aligns them in a piecewise manner.Assuming the stable areas on monitored surfaces are locally planar, supervoxel-based segmentation is employed to generate small planes from adjacent point clouds. These planes are then refined and classified by comparing defined correspondence distances to a monotonically decreasing distance threshold, thus progressively eliminating unstable planes in an efficient iterative process as well as preventing local minimization in the ICP process. Finally, point-to-plane ICP is performed on the centroids of the remaining stable planes. We introduce the level of detection in change analysis to determine the minimum distance threshold, which mitigates the influence of outliers and deformed areas on registration accuracy. Besides, the spatial distribution of empirical registration uncertainties on registered point clouds is derived based on the variance-covariance propagation law.Our registration method is demonstrated on two datasets: (1) Synthetic point cloud time series with defined changes and transformation parameters, and (2) a 4DPC dataset from a PLS system installed in the Vals Valley (Tyrol, Austria) for monitoring a rockfall. The experimental results show that the proposed algorithm exhibits higher registration accuracy compared to the existing robust ICP variants. The real-time capability of Piecewise-ICP is significantly improved owing to the centroid-based point-to-plane ICP and the efficient iteration process.

Abstract. The registration of ground laser point cloud and close-range image is the key content of high-precision 3D reconstruction of cultural relic object. In view of the requirement of high texture resolution in the field of cultural relic at present, The registration of point cloud and image data in object reconstruction will result in the problem of point cloud to multiple images. In the current commercial software, the two pairs of registration of the two kinds of data are realized by manually dividing point cloud data, manual matching point cloud and image data, manually selecting a two – dimensional point of the same name of the image and the point cloud, and the process not only greatly reduces the working efficiency, but also affects the precision of the registration of the two, and causes the problem of the color point cloud texture joint. In order to solve the above problems, this paper takes the whole object image as the intermediate data, and uses the matching technology to realize the automatic one-to-one correspondence between the point cloud and multiple images. The matching of point cloud center projection reflection intensity image and optical image is applied to realize the automatic matching of the same name feature points, and the Rodrigo matrix spatial similarity transformation model and weight selection iteration are used to realize the automatic registration of the two kinds of data with high accuracy. This method is expected to serve for the high precision and high efficiency automatic 3D reconstruction of cultural relic objects, which has certain scientific research value and practical significance.

With the rapid development of binocular reconstruction, fringe projection profilometry, and time of flight, 3D imaging technology has been widely applied in the field of 3D measurement. However, due to limited measurement range and self-occlusion, point cloud registration methods are often used to obtain larger or more complete 3D contours. Although many scholars have proposed various point cloud registration methods, the accuracy and efficiency of point cloud registration still need to be further improved, especially for point clouds with different density or non-rigid transformation. Image registration technology based on image correlation has been developed for many years and has achieved great success in fields such as computer vision, photomechanics, and photogrammetry. Therefore, a simple and direct idea in this paper is to transform the point cloud registration problem into volume image correlation problem. By this, an efficient image registration method based on fast Fourier transform and an inverse compositional Gaussian Newton optimization method that only needs to calculate the Hessian matrix once can be introduced into the point cloud registration field, which can greatly improve the speed and accuracy of point cloud registration. Comparative experiments have shown that our method has doubled the accuracy and efficiency compared to the iterative closest point (ICP) method, and its practicality has also been verified in impeller reconstruction experiments.

Laser three-dimensional (3D) image is a novel non-cooperative target 3D image acquisition technology, and the improvements in detection capability and imaging accuracy of the system are critically dependent on efficient echo-signal processing technique and 3D reconstruction method. The registration process is an essential step in array 3D imaging laser point cloud data processing. Registration of point clouds is an effective method that solves the problem caused by the target self-occlusion in the laser 3D imaging system. The accurate registration result will help provide better support for subsequent applications, such as object reconstruction and target recognition. In this study, a set of thresholds in the iterative closest point (ICP) algorithm is analysed on the basis of the characteristics of the laser array 3D imaging system and is combined with the range error and visual lateral resolution of the system, which are both important parameters in the imaging system. To improve the accuracy and speed of registration, the stop threshold of the iterative algorithm and the corresponding point-distance threshold in the algorithm are established in a novel way based on the range error and visual lateral resolution of the system. This forms the foundation, based on which an adaptive threshold ICP algorithm is proposed. The principal idea of the algorithm is to improve the threshold set that has a considerable effect on the accuracy and speed of registration. At first, the characteristics of the imaging point clouds of the laser array 3D imaging system are analysed in the algorithm. Based on this analysis, the distance between the two point clouds and corresponding points with ideal registrations are estimated theoretically, according to the range error and visual lateral resolution of the system. The simulation results show that the theoretically estimated results and actual results have the same variation tendency, thus providing a theoretical basis for subsequent improvements. Next, the estimated results are added according to the iterative closest point algorithm. This implies that the registration thresholds are capable of changing and adapting under different iterations and imaging systems, thus improving the speed and accuracy of registrations. This phenomenon is not seen in other algorithms. Experiments involving laser array imaging of a point cloud and laser scanning of depth imaging data show that the algorithm is practical and effective for both imaging types of point clouds and can improve the speed and accuracy of registration notably. The effectiveness and feasibility of the proposed algorithm are thus verified. In addition, for its full consideration of the imaging system, the basic idea of the proposed algorithm can be used for designing future applications as required.

Terrestrial laser scanners have achieved great popularity in the last decade. Their easy on-site application and the possibility of flexible and high quality post processing added to their success in several fields such as architectural, archaeological, and heritage documentation. We present a method for handling the automatic registration of point clouds which are characterized by significant noise level, generally imperfect geometry and occlusions. Hereby we combine and extend already existing and established methods to facilitate the registration of point clouds without prior pre-processing. Our approach consists—similar to other methods—of three steps which are scan analysis, pair-wise registration, and global registration. To handle the abovementioned datasets we propose to use imperfect and subdivided features, and to implement Genetic Algorithms (GAs). At the same time our approach can be seen as extension to already known Genetic Algorithms used for the registration of point clouds. By implementing an adapted version of a Genetic Algorithm in the classical registration process between rough alignment and fine registration, we are able to maintain robustness and computational performance also when registering point clouds of bigger objects characterized by a notably increased number of points, a significant noise level, and occlusions. We show and discuss the successful application of the algorithm on a scene which does not consist of classical geometric primitives such as planes.

With the widespread application of large-scale 3D point cloud data in real-world scenarios, efficient and accurate point cloud registration has become a crucial challenge. We propose an end-to-end point cloud registration method based on the Transformer architecture. This method addresses the issues of low overlap and registration in large scenes, exhibiting strong algorithmic versatility and efficiency. We introduce a combination of dynamic position encoding and ternary angular position encoding within the Transformer, effectively enhancing the representation capability of point cloud data and algorithmic generality, thus better tackling point cloud registration challenges in large scenes. Additionally, to enhance the learning capacity of the attention mechanism, we employ an improved cross-attention mechanism that multiplies the softmax with adaptive weights, enabling the model to capture key information within the point cloud more accurately. In the decoding stage, we introduce a multi-scale feature fusion approach that fully exploits the multi-layer information in point cloud data, further improving registration accuracy and robustness. Through the fusion of multi-scale features, we effectively mitigate information loss and handle matching problems between point clouds of varying sizes. Experimental results demonstrate the excellence of our method in addressing low overlap and registration tasks in large scenes, validated across multiple datasets including 3DMatch, ModelNet, KITTI, and MVP-RG.

Point cloud registration is a crucial technique in photogrammetry, remote sensing, etc. A generalized 3D point cloud registration framework has been developed to estimate the optimal rigid transformation between two point clouds using 3D key point correspondences. However, challenges arise due to the uncertainty in 3D key point detection techniques and the similarity of local surface features. These factors often lead to feature descriptors establishing correspondences containing significant outliers. Current point cloud registration algorithms are typically hindered by these outliers, affecting both their efficiency and accuracy. In this paper, we propose a fast and robust point cloud registration method based on a compatibility graph and accelerated guided sampling. By constructing a compatible graph with correspondences, a minimum subset sampling method combining compatible edge sampling and compatible vertex sampling is proposed to reduce the influence of outliers on the estimation of the registration parameters. Additionally, an accelerated guided sampling strategy based on preference scores is presented, which effectively utilizes model parameters generated during the iterative process to guide the sampling toward inliers, thereby enhancing computational efficiency and the probability of estimating optimal parameters. Experiments are carried out on both synthetic and real-world data. The experimental results demonstrate that our proposed algorithm achieves a significant balance between registration accuracy and efficiency compared to state-of-the-art registration algorithms such as RANSIC and GROR. Even with up to 2000 initial correspondences and an outlier ratio of 99%, our algorithm achieves a minimum rotation error of 0.737° and a minimum translation error of 0.0201 m, completing the registration process within 1 s.

Point cloud registration is an important task in robotics and autonomous driving to estimate the ego-motion of the vehicle. Recent advances following the coarse-to-fine manner show promising potential in point cloud registration. However, existing methods rely on good superpoint correspondences, which are hard to be obtained reliably and efficiently, thus resulting in less robust and accurate point cloud registration. In this paper, we propose a novel network, named RDMNet, to find dense point correspondences coarse-to-fine and improve final pose estimation based on such reliable correspondences. Our RDMNet uses a devised 3D-RoFormer mechanism to first extract distinctive superpoints and generates reliable superpoints matches between two point clouds. The proposed 3D-RoFormer fuses 3D position information into the transformer network, efficiently exploiting point clouds’ contextual and geometric information to generate robust superpoint correspondences. RDMNet then propagates the sparse superpoints matches to dense point matches using the neighborhood information for accurate point cloud registration. We extensively evaluate our method on multiple datasets from different environments. The experimental results demonstrate that our method outperforms existing state-of-the-art approaches in all tested datasets with a strong generalization ability.

Partial point cloud registration is an essential and fundamental component of generating complete 3D shapes, aiming at converting partial scans into a unified coordinate system. However, existing point cloud registration methods suffer from inadequately rich local feature extraction and feature interaction. In addition, these methods still face challenges in modeling the global contextual information of point clouds sufficiently, which limits the improvement in registration effectiveness, especially for partial-to-partial point cloud registration with high noise. To overcome these issues, this paper proposes an enhanced PointNet and assignable weights transformer network (LFA-Net) for partial point cloud registration. The model achieves coarse-to-fine point cloud registration through three core modules. First, the Sufficient Local Feature Extraction Module (LFM) is constructed to extract various local feature information. Then, the Adequate Feature Aggregation Module (FAM) is designed to integrate the feature information from different point clouds. Finally, the Assignable Weights Transformer Module (ATM) is presented to stimulate the model’s global modeling ability during the registration process, enabling the selection of representative points for optimal point cloud registration. Extensive experiments conducted on ModelNet40 using partially overlapping point clouds illustrate the superior registration performance of LFA-Net compared with other state-of-the-art methods. Moreover, Numerous experiments on synthetic and real-word datasets further indicate that LFA-Net also has significant advantages in registering partial point clouds with noise and unseen categories, which demonstrates its excellent robustness and generalization ability for real-world practical application.

Point cloud registration is an important part of 3D point cloud map construction based on multi-line lidar. At present, traditional iterative nearest point algorithm (ICP) is mainly used for point cloud registration. For the traditional iterative nearest point algorithm, point cloud registration is time-consuming and error-prone, which leads to the low accuracy of point cloud registration. Based on the principle of traditional iterative nearest point algorithm, this paper proposes an improved ICP algorithm combining PL-ICP algorithm and PP-ICP algorithm. The map construction is realized by this method, and on the basis of the map construction, NDT algorithm is used to further realize the positioning of unmanned vehicles. The experimental results show that the method can effectively construct the point cloud image, reduce the registration time of point cloud, and has good registration accuracy. Through the validation of the dataset, the positioning experiment can be completed by randomly placing the unmanned vehicle in a certain position on the constructed map.

Scanned data often contain substantial outliers due to environmental interference, which drastically decreases the performance of traditional registration algorithms. To address this issue, this article proposes an improved principal component analysis (PCA) and Jacobian-enhanced whale optimization collaborative method for point cloud registration. First, an improved PCA point cloud initial registration algorithm is proposed by introducing the normal vector local information to set the screening conditions. This algorithm can streamline the original set of 48 candidate rotation matrices down to 4, achieving rapid point cloud registration at the data level between the scanned and model point clouds. Second, a Jacobian whale optimization algorithm for fine registration (JWOA-FR) is proposed by incorporating local gradient information. The algorithm employs gradient descent on optimal whale individuals to dynamically guide global search updates, thereby enhancing both registration accuracy and efficiency. Finally, a threshold is set to remove the outliers contained in the workpieces based on the information of the matched point pairs. The iterative closest point (ICP) algorithm is further used to improve registration accuracy for data without outliers. The experimental results showed that registration errors of large workpieces 1, 2, and 3 were 2.0755 mm, 2.3955 mm, and 2.5823 mm, respectively, after outlier removal, which indicates that the proposed method is applicable to data with outliers, and the registration accuracy meets the requirements.

The 3D laser scanning technology has been widely adopted in the field of workshop automatic assembly. Among them, the point cloud registration is a crucial part of the 3D data processing. Among the 3D data processing steps, point cloud registration is an important one. However, existing 3D point cloud registration methods have issues such as mismatching of feature point pairs, long registration time, and poor registration accuracy. In this paper, an LM-NDT point cloud registration algorithm based on 3D shape context feature (3DSC) is proposed.Firstly, the key points of the 3D point cloud are extracted through the intrinsic shape signature (ISS) to enhance the registration efficiency. Then, the features of the key points are described by the 3D shape context feature. According to the feature points, the error pair is processed using the median distance method, and the initial transformation matrix is calculated through SVD decomposition. Finally, the alignment is accomplished by the LM-NDT algorithm. Compared with the NDT algorithm, the proposed algorithm is more accurate and robust when the initial pose transformation is more significant. The test results demonstrate that the accuracy of the algorithm in the shoe and sole can reach 0.019cm, which remarkably improves the registration efficiency of the traditional SAC-IA and LM-NDT algorithms and has certain engineering application value.

Point cloud registration, which estimates a rigid transformation matrix between two point clouds, is a fundamental process in numerous applications. While existing detector-free techniques present exceptional performance, they overlook the extraction of hybrid local features that capture correlations between points and their neighbours, thereby limiting the quality of point cloud recognition. Moreover, these approaches typically treat point clouds as sequential data and employ the transformer to integrate global context from all points, which inevitably introduces interference from irrelevant regions, hence affecting the registration accuracy. In this work, we propose a novel detector-free approach AGHL to address these challenges. For the first issue, AGHL introduces a hybrid local feature perception module that designs two parallel branches to concurrently extract low-level and high-level local features, which effectively encode the correlations between each point and its neighborhood points in both Euclidean space and high-dimensional feature space. For the second issue, AGHL develops an anchor-guided cross attention that adheres to the local geometric consistency to constrain the network's attention on reliable anchors, thereby effectively suppressing interference from irrelevant regions. Benefiting from these techniques, AGHL achieves impressive point cloud registration accuracy across all synthetic, indoor, and outdoor datasets. Furthermore, we build an experimental platform and conduct a real-world robot localization experiment, with results showing the strong generalization ability of AGHL.

The registration of 3-D point clouds is an important procedure during the terrestrial laser scanning data processing. Recently, due to their high flexibility and the powerful mathematical model, a large amount of least-squares-based (LSs-based) methods are proposed and widely applied to estimate the transformation parameters of 3-D point clouds registration. In these LSs-based methods some based on the generalized Gauss–Markov model do not correct the influence of random errors on source 3-D point clouds. Although there are other methods based on the errors-in-variables (EIV) model, they are inapplicable for transformation problems with large rotation angles and arbitrary scale ratio. In addition, the gross errors are usually ignored in previous studies on 3-D point clouds registration, which, however, exists commonly and could distort the registration severely. Aiming to avoid the influence of gross errors and extend its application, an advanced outlier detected total least-squares (OD-TLS) method is proposed in this paper. Based on the generalized EIV model OD-TLS performs a seven-parameter 3-D similarity transformation with large rotation angles and arbitrary scale ratio. The random errors of both source and target 3-D point clouds are considered. Furthermore, outliers are detected and removed automatically by combining the data snooping method with total least-squares (TLS) estimation. In order to indicate the benefits of OD-TLS, comparative experiments with the LS3D and weighted total least squares (WTLS) on synthetic and real-world scanned 3-D point clouds were performed. The experimental results show OD-TLS not only enhances the registration accuracy but also increases its robustness.

In recent years, laser scanning systems have been widely used to acquire multi-level three-dimensional spatial objects in real time. The laser scanning system is used to acquire the three-dimensional point cloud data of urban scenes. Due to the large-scale characteristics of urban scenes, and the problems of scanning occlusion, scanning path, and limited scanning laser range, the laser scanning system cannot scan every object in the scene comprehensively, multidirectionally and finely, so the corresponding three-dimensional point cloud data collected by many objects are incomplete, and the data images are relatively sparse and unevenly distributed. The existing point cloud denoising and enhancement algorithms, such as AMLS, RMLS, LOP, and WLOP, all use local information to enhance the missing or sparse parts of the point cloud. This point cloud enhancement method is only limited to a small range and cannot do anything for the larger missing area of the point cloud. Even if it is done reluctantly, the effect is not satisfactory. There are a lot of repetitive and similar features in urban buildings, such as the repetitive areas of floors and balconies in buildings. These repetitive areas are distributed in different positions of point clouds, so the repetitive information has non local characteristics. Based on the nonlocal characteristics of building point cloud data and the repetitive structure of buildings, this article proposes a nonlocal point cloud data enhancement algorithm, which organizes the point cloud data in the repeated area into a set of basic geometric elements (planes). The structures are registered in a unified coordinate system, and the point cloud is enhanced and denoised through two denoising processes, “out-of-plane” and “in-plane.”