A Survey of Rigid 3D Pointcloud Registration Algorithms (original) (raw)

A Benchmark Survey of Rigid 3D Point Cloud Registration Algorithms

Advanced user interface sensors are able to observe the environment in three dimensions with the use of specific optical techniques such as time-of-flight, structured light or stereo vision. Due to the success of modern sensors, which are able to fuse depth and color information of the environment, a new focus on different domains appears. This survey studies different state-of-the-art registration algorithms, which are able to determine the motion between two corresponding 3D point clouds. This survey starts from a mathematical field of view by explaining two deterministic methods, namely Principle Component Analysis (PCA) and Singular Value Decomposition (SVD), towards more iteratively methods such as Iterative Closest Point (ICP) and its variants. We compare the performance of the different algorithms to their precision and robustness based on a real world dataset. The main contribution of this survey consists of the performance benchmark that is based on a real world dataset, wh...

Registration with the Point Cloud Library A Modular Framework for Aligning in 3-D

—Registration is an important step when processing 3D point clouds. Applications for registration range from object modeling and tracking to simultaneous localization and mapping. This article presents the open-source Point Cloud Library (PCL) and the tools therein available for the task of point cloud registration. PCL incorporates methods for the initial alignment of point clouds using a variety of local shape feature descriptors as well as for refining initial alignments using different variants of the well-known Iterative Closest Point (ICP) algorithm. The article provides an overview on registration algorithms, usage examples of their PCL implementations, and tips for their application. Since the choice and parameterization of the right algorithm for a particular type of data is one of the biggest problems in 3D point cloud registration, we present three complete examples of data (and applications) and the respective registration pipeline in PCL. These examples include dense RGB-D point clouds acquired by consumer color and depth cameras, high-resolution laser scans from commercial 3D scanners, and low-resolution sparse point clouds captured by a custom lightweight 3D scanner on a micro aerial vehicle.

Registration of point cloud data from a geometric optimization perspective

2004

We propose a framework for pairwise registration of shapes represented by point cloud data (PCD). We assume that the points are sampled from a surface and formulate the problem of aligning two PCDs as a minimization of the squared distance between the underlying surfaces. Local quadratic approximants of the squared distance function are used to develop a linear system whose solution gives the best aligning rigid transform for the given pair of point clouds. The rigid transform is applied and the linear system corresponding to the new orientation is build. This process is iterated until it converges. The point-to-point and the point-to-plane Iterated Closest Point (ICP) algorithms can be treated as special cases in this framework. Our algorithm can align PCDs even when they are placed far apart, and is experimentally found to be more stable than point-to-plane ICP. We analyze the convergence behavior of our algorithm and of point-to-point and point-to-plane ICP under our proposed framework, and derive bounds on their rate of convergence. We compare the stability and convergence properties of our algorithm with other registration algorithms on a variety of scanned data.

Robust Registration Method of 3D Point Cloud Data

Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods, 2016

3D point cloud data is used for 3D model acquisition, geometry processing and 3D inspection. Registration of 3D point cloud data is crucial for each field. The difference between 2D image registration and 3D point cloud registration is that the latter requires several things to be considered: translation on each plane, rotation, tilt and etc. This paper describes a method of registering 3D point cloud data with noise. The relationship between the two sets of 3D point cloud data can be obtained by Affine transformation. In order to calculate 3D Affine transformation matrix, corresponding points are required. To find the corresponding points, we use the height map which is projected from 3D point cloud data onto XY plane. We formulate the height map matching as a cost function and estimate the corresponding points. To find the proper 3D Affine transformation matrix, we formulate a cost function which uses the relationship of the corresponding points. Also the proper 3D Affine transformation matrix can be calculated by minimizing the cost function. The experimental results show that the proposed method can be applied to various objects and gives better performance than the previous work.

A New Variant of the ICP Algorithm for Pairwise 3D Point Cloud Registration

2022

Pairwise 3D point cloud registration derived from Terrestrial Laser Scanner (TLS) in static mode is an essential task to produce locally consistent 3D point clouds. In this work, the contributions are twofold. First, a non-iterative scheme by merging the SIFT (Scale Invariant Feature Transform) 3D algorithm and the PFH (Point Feature Histograms) algorithm to find initial approximation of the transformation parameters is proposed. Then, a correspondence model based on a new variant of the ICP (Iterative Closest Point) algorithm to refine the transformation parameters is also proposed. To evaluate the local consistency of the pairwise 3D point cloud registration is used a point-to-distance approach. Experiments were performed using seven pairs of 3D point clouds into an urban area. The results obtained showed that the method achieves point-to-plane RMSE (Root of the Mean Square Error) mean values in the order of 2 centimeters

Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid

IEEE Transactions on Visualization and Computer Graphics, 2000

3D surface registration transforms multiple 3D datasets into the same coordinate system so as to align overlapping components of these sets. Recent surveys have covered different aspects of either rigid or non-rigid registration, but seldom discuss them as a whole. Our study serves two purposes: (i) to give a comprehensive survey of both types of registration, focusing on 3D point clouds and meshes, and (ii) to provide a better understanding of registration from the perspective of data fitting. Registration is closely related to data fitting in that it comprises three core interwoven components: model selection, correspondences & constraints and optimization. Study of these components (i) provides a basis for comparison of the novelties of different techniques, (ii) reveals the similarity of rigid and non-rigid registration in terms of problem representations, and (iii) shows how over-fitting arises in non-rigid registration and the reasons for increasing interest in intrinsic techniques. We further summarise some practical issues of registration which include initializations and evaluations, and discuss some of our own observations, insights and foreseeable research trends.

Challenging data sets for point cloud registration algorithms

International Journal of Robotics Research, 2012

Many registration solutions have bloomed lately in the literature. The iterative closest point, for example, could be considered as the backbone of many laser-based localization and mapping systems. Although they are widely used, it is a common challenge to compare registration solutions on a fair base. The main limitation is to overcome the lack of accurate ground truth in current data sets, which usually cover environments only over a small range of organization levels. In computer vision, the Stanford 3D Scanning Repository pushed forward point cloud registration algorithms and object modeling fields by providing high-quality scanned objects with precise localization. We aim at providing similar high-caliber working material to the robotic and computer vision communities but with sceneries instead of objects. We propose 8 point cloud sequences acquired in locations covering the environment diversity that modern robots are susceptible to encounter, ranging from inside an apartment to a woodland area. The core of the data sets consists of 3D laser point clouds for which supporting data (Gravity, Magnetic North and GPS) are given at each pose. A special effort has been made to ensure a global positioning of the scanner within millimeter range precision, independently of environmental conditions. This will allow for the development of improved registration algorithms when mapping challenging environments, such as found in real world situations.

A comprehensive survey on point cloud registration

arXiv: Computer Vision and Pattern Recognition, 2021

Registration is a transformation estimation problem between two point clouds, which has a unique and critical role in numerous computer vision applications. The developments of optimization-based methods and deep learning methods have improved registration robustness and efficiency. Recently, the combinations of optimization-based and deep learning methods have further improved performance. However, the connections between optimization-based and deep learning methods are still unclear. Moreover, with the recent development of 3D sensors and 3D reconstruction techniques, a new research direction emerges to align cross-source point clouds. This survey conducts a comprehensive survey, including both same-source and cross-source registration methods, and summarize the connections between optimization-based and deep learning methods, to provide further research insight. This survey also builds a new benchmark to evaluate the state-of-the-art registration algorithms in solving cross-source challenges. Besides, this survey summarizes the benchmark data sets and discusses point cloud registration applications across various domains. Finally, this survey proposes potential research directions in this rapidly growing field.

Point-cloud registration using 3D shape contexts

2012

The problem of aligning scans from a range sensor is central to 3D mapping for robots. In previous work we demonstrated a lightweight descriptor-based registration method that is suitable for creating maps from range images produced by devices such as the XBOX Kinect. For computational reasons, simple descriptors were used based only on the distribution of distances between points. In this paper, we present an alternative approach using 3D Shape Contexts that also retains angular information thereby producing descriptors that are more unique. Although this increases the computational load, intrinsic properties of the descriptor facilitate keypoint selection, leading to a more robust registration framework. This also provides greater flexibility when applying the method to sparse point clouds such as those produced by laser range scanners. Results are shown for registering new data acquired from an underground mine environment.

Point Cloud Registration Based on Image Correspondences

International Journal of Heritage in the Digital Era, 2013

Since the early '80s, when Digital Photogrammetry was in its infancy, important progress has been made in the area of automations using the most common photogrammetric procedures. The main objective of this paper is the full automation of 3D Point Cloud Registration process. In particular, an alternative registration method is being presented, based on corresponding points, which are detected on overlapping images that come with each scan using a structured light scanner. The algorithm detects corresponding points applying Feature Based Matching techniques. These are then interpolated directly to any given texture map, thereby the transition to correspondent 3D vertices is achieved. Finally, the algorithm computes the 3D Rigid Body transformation, which is applied to the 3D point clouds, in order to transform one scan's reference system into the other. Experimental results obtained by the proposed method are presented, evaluated and compared with those obtained by standard ICP implementation.