Point Cloud Registration Algorithm Research Articles

Towards a Fully Automated 3D Reconstruction System Based on LiDAR and GNSS in Challenging Scenarios

High-precision 3D maps play an important role in autonomous driving. The current mapping system performs well in most circumstances. However, it still encounters difficulties in the case of the Global Navigation Satellite System (GNSS) signal blockage, when surrounded by too many moving objects, or when mapping a featureless environment. In these challenging scenarios, either the global navigation approach or the local navigation approach will degenerate. With the aim of developing a degeneracy-aware robust mapping system, this paper analyzes the possible degeneration states for different navigation sources and proposes a new degeneration indicator for the point cloud registration algorithm. The proposed degeneracy indicator could then be seamlessly integrated into the factor graph-based mapping framework. Extensive experiments on real-world datasets demonstrate that the proposed 3D reconstruction system based on GNSS and Light Detection and Ranging (LiDAR) sensors can map challenging scenarios with high precision.

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.

Large-size sprocket repairing based on robotic GMAW additive manufacturing

The variable and irregular surface damage of large-size sprockets has led to many challenges in the robotic GMAW-based automatic repair, which is a problem that has not been solved well. In this paper, a highly adaptive method based on robotic GMAW additive manufacturing was developed to repair the large-size sprocket with high quality. Key technologies including 3d rebuilding of damage surface and automatic robot deposition path planning have been researched. The model of damaged sprocket surface was obtained by point cloud registration algorithm including improved Iterative Closest Point (ICP) and Boolean subtraction operations. A highly adaptive slicing and path planning method for repairing sprockets with variable and irregular surface damage was proposed. Deposition parameters were obtained through experiments and a database with neural networks. The robot repairing process was simulated by 3D animation before executing the robot codes. The developed robot GMAW repairing system has integrated 3D scan modeling, slicing, path planning, parameters planning, and 3D animation simulation. The validation experiment results of repairing the damaged sprockets showed that the developed system has strong adaptability and high efficiency, and the repair accuracy met the actual requirements.

SVS-NLMS point cloud registration algorithm based on geometric algebra

SVS-NLMS point cloud registration algorithm based on geometric algebra

Point Cloud Registration Algorithm Based on Laplace Mixture Model

Registering point clouds quickly and accurately has always been a challenging task. A lot of research based on Gaussian mixture model is widely used in recent years. However, few people use other models for point cloud matching. Therefore, this paper proposes a point cloud registration algorithm based on the Laplace mixture model. In this paper, sampling variance is used to replace the variance of the likelihood estimation to successfully overcome the nonlinear problem. In addition, the Laplace model has strong robustness, which is very suitable for point cloud matching of 3D laser scanning. In the experiment, compared with several other algorithms, proposed method quickly and accurately registers point clouds.

一种改进的基于快速点特征直方图的ICP点云配准算法

一种改进的基于快速点特征直方图的ICP点云配准算法

Point Cloud Registration Algorithm Based on Cauchy Mixture Model

In this paper, the Cauchy mixture distribution is used for rigid and affine registration. The Cauchy function has a wider effective range than the general window function, so it has a greater information utilization ratio than most registration algorithms. This means that the method can quickly match point clouds without initial registration and corresponding points. On the other hand, the computational complexity of Cauchy mixture model is lower than that of widely used Gaussian mixture model. This means that the introduction of Cauchy mixture model will save computing resources. However, the log-likelihood function of the Cauchy mixture distribution is a nonlinear function that is difficult to solve. This paper proposes an optimization method for the Cauchy mixture model, and discusses the advantages of the Cauchy mixture model in rigid and affine registration. In the experiment, compared with several other algorithms, our method quickly and accurately registers point clouds.

Probability driven approach for point cloud registration of indoor scene

Point cloud registration is a crucial step of localization and mapping with mobile robots or in object modeling pipelines. In this paper, we present a novel probability driven algorithm for point cloud registration of the indoor scene based on RGB-D images. Firstly, we extract the key points in RGB-D images and map the key points to 3D space as preprocessing. Then, we build the distance matrix and the difference matrix for each point cloud, respectively in scalarization and vectorization, to encode the structural proximity. And establish the corresponding point set by computing the matching probabilities. At last, we solve the transform matrix that aligns the source point cloud to the target point cloud. The entire registration framework consists of two phases: coarse registration based on the distance matrix (in scalarization) and fine registration based on the difference matrix (in vectorization). The two-phase registration strategy is able to greatly reduce the influence of inherent noise. Experiments demonstrate that our method outperforms in registration accuracy than the state-of-the-art methods. Furthermore, our method is more efficient than existing methods in computing speed because we utilize the location relationship between key points instead of point features. The source code is provided at our project website https://github.com/BeCoolGuy/Probability-Driven-Approach-for-Point-Cloud-Registration-of-Indoor-Scene .

Automatic 3D point cloud registration algorithm based on triangle similarity ratio consistency

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.

Pose estimation and adaptable grasp configuration with point cloud registration and geometry understanding for fruit grasp planning

Picking and placing fruits is a fundamental capability and common operation for agricultural robots in harvesting, sorting and packing tasks. Secure grasping fruits without potential slip and damage is challenging due to the shape varying, bruise sensitizing, as well as hardness changing during fruit maturation. In this study, a state-of-art point cloud processing based pre-grasp planning method was proposed to complete accurate 6D pose estimation and adaptable grasp configuration with point cloud registration and geometry understanding algorithm for grasp planning. Fruits 6D pose was estimated with SAC-IA ICP algorithm by matching Kinect-based online target point cloud obtained from 3D reconstruction with laser scanner based offline template. Simultaneously, fruits geometry understanding is completed with RANSAC algorithm, and the shape of typical fruits were classified into sphere, cylinder and cone. To pre-define grasp configuration for all kinds of shapes, fruits mesh models are also created with greedy projection algorithm for rapid collision detection and contact determination in GraspIt. Finally, the grasp information can be retrieved and transformed to match the position and orientation of the object by combining the results of 6D pose and geometry understanding. Compared with deep learning based pose estimation algorithm, large training data set for each novel, unknown object is not required in our method. The performance of the proposed system and algorithm has been evaluated on orange, cucumber, pineapple, kiwifruit and other agricultural products. The result indicates that our method is reliable with high efficiency and accuracy, and the 6D pose estimation along with adaptable grasp configuration results can provide a theoretical basis for grasp planning.

Automatic point cloud registration algorithm based on the feature histogram of local surface.

In this paper, we present an efficient algorithm for point cloud registration in presence of low overlap rate and high noise. The proposed registration method mainly includes four parts: the loop voxel filtering, the curvature-based key point selection, the robust geometric descriptor, and the determining and optimization of correspondences based on key point spatial relationship. The loop voxel filtering filters point clouds to a specified resolution. We propose a key point selection algorithm which has a better anti-noise and fast ability. The feature descriptor of key points is highly exclusive which is based on the geometric relationship between the neighborhood points and the center of gravity of the neighborhood. The correspondences in the pair of two point clouds are determined according to the combined features of key points. Finally, the singular value decomposition and ICP algorithm are applied to align two point clouds. The proposed registration method can accurately and quickly register point clouds of different resolutions in noisy situations. We validate our proposal by presenting a quantitative experimental comparison with state-of-the-art methods. Experimental results show that the proposed point cloud registration algorithm has faster calculation speed, higher registration accuracy, and better anti-noise performance.

Point Cloud Registration Algorithm Based on Overlapping Region Extraction

During the process of the point cloud registration, the problem that the accuracy is not high is due to the unknown relative position of the multi-view point clouds and the diversity of the target structure. This paper proposes a point cloud registration method based on extracting overlapping regions to solve it. First of all, according to the characteristics of the target geometric structure, each point cloud is divided into blocks and the ESF multi-dimensional shape descriptors of each point cloud block are constructed, the region with the greatest similarity between the descriptors, namely the overlapping region between the point clouds. Then our algorithm uses the Super Four Point Fast Robust Matching (Super4PCS) algorithm to execute a coarse registration of point clouds in overlapping regions, the initial positions of the point clouds after the coarse registration are obtained according to the consistency constraints, and finally uses the iterative closest point algorithm (ICP) to precisely register the overlapping region and obtain the final point cloud model. Compared with traditional Super4PCS algorithm, the experimental results show that the method proposed in this paper effectively improves the accuracy and accelerates the process of point cloud registration.

The Semi-dense ICP Algorithm Based on the SIFT Feature Points Neighborhood

The ICP algorithm based on RGB-D images is one of the most widely used and the most effective point cloud registration algorithms. How to improve the registration accuracy, registration speed and robustness of the ICP algorithm has become an important research hotspot at present. However, existing algorithms have low computational efficiency under large-scale point cloud data, and when the overlap between point clouds is small, the registration accuracy is low. To deal with these problems, this paper proposes a semi-dense ICP algorithm based on SIFT feature points neighbourhood. First, the algorithm selects the SIFT feature point matching algorithm as the rough registration method of ICP, because the SIFT feature points have the advantage of rotation invariance, illumination invariance, and scale invariance; then based on the SIFT feature points, the algorithm selecting its neighbourhood as the matching range. The experimental result shows that this algorithm has the better registration accuracy and computational efficiency.

ScaleLK: Registration of Point Clouds with Different Scales Using Deep Learning Methods

3D point clouds are widely used in numerous research and applications, such as autonomous driving, industrial robots, and augmented reality, to represent the spatial structure of objects. The 3D point cloud registration aims to transform the source point cloud into the same coordinate system with the template point cloud, which is of great significance for the 3D reconstruction of the real world objects. ICP [1] is one of the most classic point cloud registration algorithms but it still has problems with efficiency and initialization. With the help of deep learning, PointNetLK [7] becomes a state-of-the-art point cloud registration method. Although PointNetLK is efficient and robust to some extent, it is not able to register point clouds with different scales. In this paper, we propose ScaleLK, an approach for registration of point clouds with different scales using deep learning methods. We have trained a feature extractor which supports scale feature and used this feature for registration of point clouds with different scales. We describe the architecture and compare its performance with other methods.

Point cloud registration algorithm based on the volume constraint

Point cloud registration algorithm based on the volume constraint

An Accelerated and Robust Partial Registration Algorithm for Point Clouds

Partial registration for point clouds plays an important role in various fields such as 3D mapping reconstruction, remote sensing, unmanned driving, and cultural heritage protection. Unfortunately, partial registration is challenging due to difficulties such as the low overlap ratio of two point clouds and the perturbation in the orderless and sparse 3D point clouds. Thus, a variety of the 3D shape context descriptors are introduced for finding the optimal matching. However, extracting geometric features and descriptors are time consuming and easily degenerated by noise. To overcome these problems, we introduce a parallel coarse-to-fine partial registration method. Our contributions can be summarized as: Firstly, a robust coarse trimmed method is proposed to estimate the coarse overlap area and the initial transformation via fast bilateral denoising and parallel point feature histogram (PPFH) descriptor aligning. Secondly, an accelerated fine registration procedure is conducted by a parallel trimmed iterative closest point (PTrICP) method. Moreover, most parts of our coarse-to-fine workflow are accelerated under the Graphics Processing Unit (GPU) parallel execution mode for efficiency. Thirdly, we extend our method from the rigid registration to the isotropic scaling registration, which improves its applicability. Experiments have demonstrated that our method is feasible and robust in various situations, including the low overlap ratio, outlier, noise and scaling.

Point Cloud Registration Algorithm Based on the Grey Wolf Optimizer

In view of the long computation time and low registration accuracy of the current point cloud registration algorithm, a point cloud registration algorithm based on the grey wolf optimizer (GWO) is proposed, denoted PCR-GW. The algorithm uses the centralization method to solve the translation matrix and then simplifies the points of the initial point cloud models by using the intrinsic shape signatures (ISS) feature. Next, various parameters of the rotation matrix are obtained via the GWO algorithm by employing the quadratic sum of the distances between corresponding points in the simplified point cloud as the objective function. Finally, the point cloud registration process is completed by using the obtained transformation matrix. By conducting a registration experiment on the point cloud library model and comparing PCR-GW with the traditional algorithms, the algorithm proposed in this article is shown to be promising for improving the computation speed and registration accuracy.

平面特征约束下基于四元数描述的LiDAR点云配准算法

平面特征约束下基于四元数描述的LiDAR点云配准算法

基于电塔快速提取的点云配准算法研究

基于电塔快速提取的点云配准算法研究

Comparison of Several Different Registration Algorithms

Abstract The common point cloud registration algorithms are usually divided into initial registration and precise registration. In this paper, SAC-IA algorithm, which is commonly used in PCL, is selected for initial registration, and the traditional ICP algorithm is used for accurate registration. Three different feature descriptors (3D shape context, Point Feature Histograms, Fast Point Feature Histograms) are used to realize SAC-IA algorithm and ICP precise registration algorithm. During the implementation of the algorithm, the registration time and registration error of point cloud are calculated; according to the experimental results, the registration time and registration error of SAC-IA algorithm and ICP algorithm based on three different descriptors are compared. The results show that the registration algorithm based on 3D shape context has high accuracy, but the registration time is too long, which is not suitable for a large number of point cloud data; the registration algorithm based on fast point feature histograms has short registration time and good registration effect.