Robust registration of 3D point clouds using GA with adaptable boundary constraints

This paper reports a novel mutual information (MI) based algorithm for automatic registration of unstructured 3D point clouds comprised of co-registered 3D lidar and camera imagery. The proposed method provides a robust and principled framework for fusing the complementary information obtained from these two different sensing modalities. High-dimensional features are extracted from a training set of textured point clouds (scans) and hierarchical k-means clustering is used to quantize these features into a set of codewords. Using this codebook, any new scan can be represented as a collection of codewords. Under the correct rigid-body transformation aligning two overlapping scans, the MI between the codewords present in the scans is maximized. We apply a James-Stein-type shrinkage estimator to estimate the true MI from the marginal and joint histograms of the codewords extracted from the scans. Experimental results using scans obtained by a vehicle equipped with a 3D laser scanner and an omnidirectional camera are used to validate the robustness of the proposed algorithm over a wide range of initial conditions. We also show that the proposed method works well with 3D data alone.

Three‐dimensional (3D) point cloud registration is a fundamental key issue in 3D reconstruction, 3D object recognition and augmented reality. In this study, the authors propose a novel local feature descriptor called local angle statistics histogram (LASH) for efficient 3D point cloud registration. LASH forms a description of local shape geometries by encoding their properties on angles between the normal vector of the point and the vector formed by the point and other points in its local neighbourhood. In addition, they propose a 3D point cloud registration algorithm based on LASH. The registration algorithm firstly detects triangle matching points with consistent similarity ratios, and then aggregates each pair of triangular matching points into a set of matching points. They can use these matching sets to calculate multiple transformations between two point clouds. Finally, they use the error function to identify the best transformation and to achieve coarse alignment of the two point clouds. Experiments and comparisons with other global algorithms demonstrate that the proposed approach can be applied to register point clouds with considerable or limited overlaps and is robust to noise.

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.

Coded light measurement is an important method of non-contact optical measurement. Because of the camera view limitations, it was hard to complete the measurement of the whole surface at a time. Then it needs registration of 3D point clouds. The registration of point clouds can reflect the 3D surface of the object to be measured, which can achieve the full range of requirements. According to the encoded optical 3D measurement system has been built in laboratory, exploring a method which is high accurate and high speed to reach a full range measurement to industrial parts. This paper describes the color target cube. According to the existing method of coordinate transformation (the Nearest Point Iterative Method), Evaluating the registration accuracy. The registration method based on auxiliary three-dimensional targets was been validated, which has its own rationality.

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.

Because of its high sensitivity to misalignment, precision grinding of free-form surfaces with micron accuracy requires accurate registration of the surface measurement point cloud. Registration of point clouds obtained with a coordinate measuring machine (CMM) is generally an iterative process of finding optimal coordinate transformation between the CMM frame and the model frame of the workpiece by minimizing the point-to-surface distances with probe radius compensation. For free-form surfaces, frequent calculation of point-to-surface distances consumes very much time, and a trade-off has to be made between the efficiency and the accuracy. This paper presents a method for fast registration of free-form surface point clouds based on the point-to-triangle distance which involves only Delaunay triangulation of a two-dimensional dataset, and the surface normal is quickly calculated from cross product. Probe radius compensation is realized by registering the probe center points with the offset surface. We prove that it is equivalent to registering the probe contact points with the nominal surface through theoretical analysis. The registration problem is then formulated as sequential linear least-square problems with properly defined ball constraints. To validate the method, numerical simulations are presented to show the accuracy of the point-to-triangle distance. The registration algorithm also shows excellent robustness against misalignment of tens of millimeters/degrees. Finally measurement, registration, and grinding of a free-form optical surface are experimentally demonstrated. The surface error obtained after registration is used for compensatory grinding which reduces it to micron level.

Registering 3D point clouds with low overlap is challenging in 3D computer vision, primarily due to difficulties in identifying small overlap regions and removing correspondence outliers. We observe that the neighborhood similarity can be utilized to detect point correspondence, and the consistent neighborhood correspondence can be used as a criterion to detect robust overlapping regions. So that a Double-layer Multi-scale Star-graph (DMS) structure is proposed to detect robust correspondences using two different types of multi-scale star-graphs. The first-layer Multi-scale Neighbor Feature Star-graphs (MNFS) takes each point as the center and its multi-scale nearest neighbors as the leaves. The MNFS enables to establish the initial correspondence candidate set between the two point clouds based on multi-scale neighborhood topology and feature similarity. Subsequently, each pair of corresponding points find their nearest neighbors within the correspondence sets to construct a Multi-scale Matching Star-graphs (MMS) on each side, so the mutual correspondence relationships between the MMS vertices are identified. These identified mutual correspondences are treated as vertices to construct the Multi-scale Correspondence Star-graphs (MCS), that indicate the relationships among the correspondences. We design edge weight and vertex weight criterion in MCS to detect only the robust correspondence set that has strong neighborhood consistency, so as to reject the outliers. Finally, the point cloud registration is conducted based on the detected robust correspondence. The experimental results demonstrate clearly that the proposed DMS method exhibits superior robustness when compared to existing state-of-the-art registration algorithms.

Recent advance in high-accuracy sensors has made point cloud become the main data format to characterize the three-dimensional world. Since the sensor can only scan and capture the 3D data within a limited field of view, an alignment algorithm is needed to generate the complete 3D scene. Point cloud registration is the solution for alignment problem that aims to estimate the transformation matrix between two frames of different point cloud sets. In this paper, we propose a neural network called OLFF-Net to achieve robust registration of 3D point clouds based on overlapped local feature fusion, which focuses on extracting rotational-invariant local features while providing enough information to achieve accurate alignment. Extensive experiments on representative datasets indicate that the framework can largely outperform competing methods with an average improvement of 16.82% in the metrics over the compared methods. More importantly, it shows significant generalization capability and can be widely applied to point cloud data with multiple complex structures.

3D point cloud registration is a key technology in 3D point cloud processing, such as 3D reconstruction, object detection. Trimmed Iterative Closest Point algorithm is a prevalent method for registration of two partially overlapping clouds. However, it relies heavily on the initial value and is liable to be trapped in to local optimum. In this paper, we adapt the Differential Evolution algorithm to obtain global optimal solution. By design appropriate evolutionary operations, the algorithm can make the populations distributed more widely, and keep the individuals from concentrating to a local optimum. In the experiment, the proposed algorithm is compared with existing methods which are based on global optimization algorithm such as Genetic Algorithm and particle filters. And the results have demonstrated that the proposed algorithm is more robust and can converge to a good result in fewer generations.

The present work proposes a new method Reg3DFacePtCd for registration of point clouds. The key contribution of the present method is that an unknown face in 3D point cloud form is given to the system and is registered to the already existing known 3D face point clouds using a fast 3D face registration method. The novelty of the present technique is that at first the alignment and registration parameters are found out by initially registering eight key points of the unknown source model to that of the known model. Next, the rest of the point clouds of the unknown model are registered to that of the known model using the same parameters found as above. The main method used for alignment is iterative closest point (ICP) using point-based technique followed by registration in the least squares sense. Mainly there are two significant contributions. Firstly, we have developed a new mathematical model facial landmark point based model across poses to obtain the target or the known model to which all the unknown models will be registered. Secondly, a novel way to accelerate point cloud matching by reducing the number of points has been proposed. Using a small number of points necessarily would speed up the registration process but may inculcate errors. So, to determine the registration quality of the fundamental eight key points on which the entire registration process is based, a new robust metric namely ICV (ICP certainty vector) consisting of several key components have been used. Finally, we have addressed several important face registration issues like pre-processing, convergence and quality of registration of the entire facial point cloud based on the eight key points. Extensive experimentation on Frav3D, GavabDB, and the Bosphorus databases on a high-performance computing environment show the novelty and robustness of the method.

3D point cloud registration is a fundamental problem in computer vision and robotics. Recently, learning-based point cloud registration methods have made great progress. However, these methods are sensitive to outliers, which lead to more incorrect correspondences. In this paper, we propose a novel deep graph matching-based framework for point cloud registration. Specifically, we first transform point clouds into graphs and extract deep features for each point. Then, we develop a module based on deep graph matching to calculate a soft correspondence matrix. By using graph matching, not only the local geometry of each point but also its structure and topology in a larger range are considered in establishing correspondences, so that more correct correspondences are found. We train the network with a loss directly defined on the correspondences, and in the test stage the soft correspondences are transformed into hard one-to-one correspondences so that registration can be performed by a correspondence-based solver. Furthermore, we introduce a transformer-based method to generate edges for graph construction, which further improves the quality of the correspondences. Extensive experiments on object-level and scene-level benchmark datasets show that the proposed method achieves state-of-the-art performance.

The Matching or Registration of 3D point clouds is a problem that arises in a variety of research areas with applications ranging from heritage reconstruction to quality control of precision parts in industrial settings. The central problem in this research area is that of receiving two point clouds, usually representing different parts of the same object and finding the best possible rigid alignment between them. Noise in data, a varying degree of overlap and different data acquisition devices make this a complex problem with a high computational cost. This issue is sometimes addressed by adding hardware to the scanning system, but this hardware is frequently expensive and bulky. We present an algorithm that makes use of cheap, widely available (smartphone) sensors to obtain extra information during data acquisition. This information then allows for fast software registration. The first such hybrid hardware-software approach was presented in [31]. In this paper we improve the performance of this algorithm by using hierarchical techniques. Experimental results using real data show how the algorithm presented greatly improves the computation time of the previous algorithm and compares favorably to state of the art algorithms.

Fine registration of 3D point clouds fusing structural and photometric information using an RGB-D camera

Coarse registration of 3D point clouds plays an indispensable role for parametric, semantically rich, and realistic digital twin buildings (DTBs) in the practice of GIScience, manufacturing, robotics, architecture, engineering, and construction. However, the existing methods have prominently been challenged by (i) the high cost of data collection for numerous existing buildings and (ii) the computational complexity from self-similar layout patterns. This paper studies the registration of two low-cost data sets, i.e., colorful 3D point clouds captured by smartphones and 2D CAD drawings, for resolving the first challenge. We propose a novel method named ‘Registration based on Architectural Reflection Detection’ (RegARD) for transforming the self-symmetries in the second challenge from a barrier of coarse registration to a facilitator. First, RegARD detects the innate architectural reflection symmetries to constrain the rotations and reduce degrees of freedom. Then, a nonlinear optimization formulation together with advanced optimization algorithms can overcome the second challenge. As a result, high-quality coarse registration and subsequent low-cost DTBs can be created with semantic components and realistic appearances. Experiments showed that the proposed method outperformed existing methods considerably in both effectiveness and efficiency, i.e., 49.88% less error and 73.13% less time, on average. The RegARD presented in this paper first contributes to coarse registration theories and exploitation of symmetries and textures in 3D point clouds and 2D CAD drawings. For practitioners in the industries, RegARD offers a new automatic solution to utilize ubiquitous smartphone sensors for massive low-cost DTBs.

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.