Point Set Registration Algorithm Research Articles

Cooperative 3D tunnel measurement based on 2D–3D registration of omnidirectional laser light

AbstractIn this study, we proposed a high‐density three‐dimensional (3D) tunnel measurement method, which estimates the pose changes of cameras based on a point set registration algorithm regarding 2D and 3D point clouds. To detect small deformations and defects, high‐density 3D measurements are necessary for tunnel construction sites. The line‐structured light method uses an omnidirectional laser to measure a high‐density cross‐section point cloud from camera images. To estimate the pose changes of cameras in tunnels, which have few textures and distinctive shapes, cooperative robots are useful because they estimate the pose by aggregating relative poses from the other robots. However, previous studies mounted several sensors for both the 3D measurement and pose estimation, increasing the size of the measurement system. Furthermore, the lack of 3D features makes it difficult to match point clouds obtained from different robots. The proposed measurement system consists of a cross‐section measurement unit and a pose estimation unit; one camera was mounted for each unit. To estimate the relative poses of the two cameras, we designed a 2D–3D registration algorithm for the omnidirectional laser light, and implemented hand‐truck and unmanned aerial vehicle systems. In the measurement of a tunnel with a width of 8.8 m and a height of 6.4 m, the error of the point cloud measured by the proposed method was 162.8 and 575.3 mm along 27 m, respectively. In a hallway measurement, the proposed method generated less errors in straight line shapes with few distinctive shapes compared with that of the 3D point set registration algorithm with Light Detection and Ranging.

Point Set Registration for Target Localization Using Unmanned Aerial Vehicles

The problem of point set registration (PSR) on images obtained using a group of unmanned aerial vehicles (UAVs) is addressed in this article. UAVs are given a flight plan each, which they execute autonomously. A flight plan consists of a series of GPS coordinates and altitudes that indicate where the UAV stops and hovers momentarily to capture an image of stationary targets on ground. A PSR algorithm is proposed that, given any two images and corresponding GPS coordinates and altitude, estimates the overlap between the images, identifies targets in the overlapping area, and matches these targets according to the geometric patterns they form. The algorithm estimates the overlap considering the error in UAVs’ locations due to wind, and it differentiates similar geometrical patterns by their GPS location. The algorithm is evaluated using the percentage of targets in the overlapping area that are matched correctly and the percentage of overlapping images matched correctly. The target-matching rate achieved using only the GPS locations of targets varied from 44% to 55% for target densities that varied from 6.4 down to 3.2 targets/m 2 . The proposed algorithm achieved target-matching rates of 48% to 87%. Well-known algorithms for PSR achieved lower rates on average.

Multi-view Image Color Consistency Enhancement using 3D Point Cloud Registration

Color inconsistency in multi-view images degrades the performance of various consumer electronics applications that use multi-view images, such as panoramic image stitching and 3D reconstruction for VR contents. In this paper, a novel approach to multi-view image color consistency enhancement is proposed. In contrast to conventional methods that try to find a nonlinear transfer function for each color channel, in the proposed method, the color distribution of the source image is non-rigidly transformed into that of the reference image using a 3D point set registration algorithm. In order to significantly reduce the computation of the 3D registration process, representative color points are utilized for the 3D registration instead of all the pixel color points. The number of representatives is much less than the number of image pixels, but they effectively approximate the color distribution of the image. Through extensive experiments, we show that the proposed method outperforms conventional methods in color consistency enhancement both subjectively and objectively, and the proposed representative color point set approximation improves the computational efficiency.

Anthropomorphic Grasping of Complex-Shaped Objects Using Imitation Learning

This paper presents an autonomous grasping approach for complex-shaped objects using an anthropomorphic robotic hand. Although human-like robotic hands have a number of distinctive advantages, most of the current autonomous robotic pickup systems still use relatively simple gripper setups such as a two-finger gripper or even a suction gripper. The main difficulty of utilizing human-like robotic hands lies in the sheer complexity of the system; it is inherently tough to plan and control the motions of the high degree of freedom (DOF) system. Although data-driven approaches have been successfully used for motion planning of various robotic systems recently, it is hard to directly apply them to high-DOF systems due to the difficulty of acquiring training data. In this paper, we propose a novel approach for grasping complex-shaped objects using a high-DOF robotic manipulation system consisting of a seven-DOF manipulator and a four-fingered robotic hand with 16 DOFs. Human demonstration data are first acquired using a virtual reality controller with 6D pose tracking and individual capacitive finger sensors. Then, the 3D shape of the manipulation target object is reconstructed from multiple depth images recorded using the wrist-mounted RGBD camera. The grasping pose for the object is estimated using a residual neural network (ResNet), K-means clustering (KNN), and a point-set registration algorithm. Then, the manipulator moves to the grasping pose following the trajectory created by dynamic movement primitives (DMPs). Finally, the robot performs one of the object-specific grasping motions learned from human demonstration. The suggested system is evaluated by an official tester using five objects with promising results.

An efficient point-set registration algorithm with dual terms based on total least squares

Point set registration (PSR) is competitive with related techniques because it purposefully captures the overall structure between two point-set patterns. Typically, the point set registration problem can be divided into two sub-problems: (1) search point set correspondence (PSC); (2) estimate spatial transformation matrix (STM). Searching for the best PSC is a classical combinatorial explosion problem, and estimating the STM is a continuous space optimization problem. Also, two feature point sets detected by two low-quality images include point-position errors and often involve bilateral outliers composed of such feature points that cannot form a correspondence relationship. To address the above problems, we propose an efficient PSR algorithm with dual (symmetrical) terms based on the total least squares (DT-TLS), which can correct errors-in-variables and suppress multiple outliers. Meanwhile, the framework of soft decision-making is presented, and a TLS-based criterion is constructed to efficiently exploit the probability and global structures of two-point sets. Such TLS-based criterion with single row-orthonormal STM includes two interesting dual (symmetrical) terms that can be conveniently exploited to suppress bilateral outliers. The experimental results show that DT-TLS achieves better performance than the state-of-the-art algorithms in some multi-view computer vision tasks, indicating that our proposed algorithm is suitable for solving PSR problems.

PREFACE: A SPECIAL SELECTION ON EMERGING TECHNIQUES FOR BIOMECHANICS–PART II

PREFACE: A SPECIAL SELECTION ON EMERGING TECHNIQUES FOR BIOMECHANICS–PART II

An Unsupervised Iterative N-Dimensional Point-Set Registration Algorithm

An unsupervised iterative N-dimensional point-set registration algorithm for unlabeled data (i.e., the correspondence between points is unknown) based on linear least squares is proposed. The algorithm considers all possible point pairings and iteratively aligns the two sets until the number of point pairs does not exceed the maximum number of allowable one-to-one pairings.

In-Hand Object Localization Using a Novel High-Resolution Visuotactile Sensor

In-hand object localization and manipulation has always been a challenging task in robotic community. In this article, we address this problem by vision-based tactile sensing with high-spatial resolution. Specifically, we design a novel tactile sensor based on stereo vision, named GelStereo, which can perceive tactile point cloud with high-spatial resolution ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&lt;\!1$</tex-math></inline-formula> mm). A tactile-based in-hand object localization pipeline composed of saliency detection and probabilistic point-set registration algorithms of the perceived contact point cloud is presented. Furthermore, extensive qualitative and quantitative analyses of perceived tactile point cloud and in-hand localization and insertion experiments of small parts are performed on our robot platform. The experimental results verify the accuracy and robustness of the tactile point cloud sensed by the novel GelStereo tactile sensor and the proposed in-hand object localization pipeline. This novel high-resolution visuotactile sensing technology has predictable application potential in the field of dexterous robotic manipulation.

An Unsupervised, Iterative N -Dimensional Point-Set Registration Algorithm

An unsupervised, iterative N -dimensional point-set registration algorithm for unlabeled data (i.e. correspondence between points is unknown) and based on linear least squares is proposed. The algorithm considers all possible point pairings and iteratively aligns the two sets until the number of point pairs does not exceed the maximum number of allowable one-to-one pairings.

Delaunay walk for fast nearest neighbor: accelerating correspondence matching for ICP

Point set registration algorithms such as Iterative Closest Point (ICP) are commonly utilized in time-constrained environments like robotics. Finding the nearest neighbor of a point in a reference 3D point set is a common operation in ICP and frequently consumes at least 90% of the computation time. We introduce a novel approach to performing the distance-based nearest neighbor step based on Delaunay triangulation. This greedy algorithm finds the nearest neighbor of a query point by traversing the edges of the Delaunay triangulation created from a reference 3D point set. Our work integrates the Delaunay traversal into the correspondences search of ICP and exploits the iterative aspect of ICP by caching previous correspondences to expedite each iteration. An algorithmic analysis and comparison is conducted showing an order of magnitude speedup for both serial and vector processor implementation.

Point Set Registration Based on Improved KL Divergence

A point set registration algorithm based on improved Kullback–Leibler (KL) divergence is proposed. Each point in the point set is represented as a Gaussian distribution. The Gaussian distribution contains the position information of the candidate point and surrounding ones. In this way, the entire point set can be modeled as a Gaussian mixture model (GMM). The registration problem of two point sets is further converted as a minimization problem of the improved KL divergence between two GMMs, and the genetic algorithm is used to optimize the solution. Experimental results show that the proposed algorithm has strong robustness to noise, outliers, and missing points, which achieves better registration accuracy than some state-of-the-art methods.

An Improved Non-Rigid Point Set Registration Algorithm by Preserving Local Topology

An Improved Non-Rigid Point Set Registration Algorithm by Preserving Local Topology

Robust and Accurate Nonrigid Point Set Registration Algorithm to Accommodate Anisotropic Positional Localization Error Based on Coherent Point Drift

Nonrigid point set (PS) registration is an outstanding and fundamental problem in the fields of robotics, computer vision, medical image analysis, and image-guided surgery (IGS). The aim of a nonrigid registration problem is to align together two point sets where one has been deformed. The assumption of isotropic localization error is shared in the previous nonrigid registration algorithms. In this article, we have derived and presented a novel nonrigid registration algorithm, where the position localization error (PLE) is generalized to be anisotropic, which means that the error distribution is not the same in different spatial directions. The motivation of considering the anisotropic characteristic is that the PLE is actually different in three spatial directions in real applications of registrations, such as IGS. Mathematically, the difficulty in dealing with the anisotropic error case comes from the change from a standard deviation that is a scalar to a covariance matrix. The formulas for updating the parameters in both expectation and maximization steps are derived. More specifically, in the expectation step, we compute the posterior probabilities that represent the correspondences between points in two PSs. In the maximization step, given the current posteriors, the covariance matrix of the PLE and the nonrigid transformation are updated. To further speed up the proposed algorithm, the low-rank approximation variation of our method is also presented. We have demonstrated through experiments on both general and medical data sets (corrupted with noise) that the proposed algorithm outperforms the state-of-the-art ones in terms of registration accuracy and robustness to noise. More specifically, all the experimental results have passed the statistical tests at the 5% significance level. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article was motivated by solving the problem of nonrigidly registering two point sets where one has been deformed and corrupted with anisotropic noise. Most existing registration methods generally assume the positional error to be the same in all directions, which in fact is not the case in real scenarios. This article presents a new robust method that assumes the positional error to be anisotropic, which is the case in point sets coming from the stereo reconstruction. The nonrigid registration problem is formulated as a maximum-likelihood (ML) problem and solved with the expectation–maximization (EM) technique. We have demonstrated through extensive experiments on both general and medical data sets that the proposed registration algorithm achieves significantly improved accuracy, robustness to noise, and outliers compared with state-of-the-art algorithms. The algorithm is particularly suitable for biomedical applications involving the registration, such as medical imaging and image-guided surgery (IGS).

Estimating the Pose of a Guinea-pig Cochlea Without Medical Imaging.

The pose (i.e., position and orientation) of a guinea-pig cochlea can be accurately estimated using externally observable features, without requiring computed-tomography (CT) scans. Guinea pigs are frequently used in otologic research as animal models of cochlear-implant surgery. In robot-assisted surgical insertion of cochlear-implant electrode arrays, knowing the cochlea pose is required. A preoperative CT scan of the guinea-pig anatomy can be labeled and registered to the surgical system, however, this process can be expensive and time consuming. Anatomical features from both sides of 11 guinea-pig CT scans were labeled and registered, forming sets. Using a groupwise point-set registration algorithm, errors in cochlea position and modiolar-axis orientation were estimated for 11 iterations of registration where each feature set was used as a hold-out set containing a reduced number of features that could all be touched by a motion-tracking probe intraoperatively. The method was validated on 2000 simulated guinea-pig cochleae and six physical guinea-pig-skull cochleae. Validation on simulated cochleae resulted in cochlea-position estimates with a maximum error of 0.43 mm and modiolar-axis orientation estimates with a maximum error of 8.1 degrees for 96.7% of cochleae. Physical validation resulted in cochlea-position estimates with a maximum error of 0.80 mm and modiolar-axis orientation estimates with a maximum error of 12.4 degrees. This work enables researchers conducting robot-assisted surgical insertions of cochlear-implant electrode arrays using a guinea-pig animal model to estimate the pose of a guinea-pig cochlea by locating six externally observable features on the guinea pig, without the need for CT scans.

A contour-guided pose alignment method based on Gaussian mixture model for precision assembly

Purpose The purpose of this study was to solve the problem of pose measurement of various parts for a precision assembly system. Design/methodology/approach A novel alignment method which can achieve high-precision pose measurement of microparts based on monocular microvision system was developed. To obtain the precise pose of parts, an area-based contour point set extraction algorithm and a point set registration algorithm were developed. First, the part positioning problem was transformed into a probability-based two-dimensional point set rigid registration problem. Then, a Gaussian mixture model was fitted to the template point set, and the contour point set is represented by hierarchical data. The maximum likelihood estimate and expectation-maximization algorithm were used to estimate the transformation parameters of the two point sets. Findings The method has been validated for accelerometer assembly on a customized assembly platform through experiments. The results reveal that the proposed method can complete letter-pedestal assembly and the swing piece-basal part assembly with a minimum gap of 10 µm. In addition, the experiments reveal that the proposed method has better robustness to noise and disturbance. Originality/value Owing to its good accuracy and robustness for the pose measurement of complex parts, this method can be easily deployed to assembly system.

Water diffusivity estimation in air-dried complex-shaped foods by the method of slopes: Application to oblate spheroid geometry

Water diffusivity is an important parameter for designing convective drying processes, as it is instrumental for determining the rate at which moisture is removed from solid foodstuffs. Most estimation methods for water diffusivity estimation rely in simple geometries and are based on analytical solutions for mass transfer under constant diffusivity in a rigid solid, assumptions that most often leads to unreliable estimations. In this study, the method of slopes is generalized to complex-shaped geometries lacking analytical solutions, allowing the estimation of variable diffusivities in shrinking solids. The method is validated with experimental drying data for fresh and osmodehydrated mushroom pilei and implemented by using the numerical solution to an unsteady state 2D drying model formulated in the oblate spheroidal coordinate system. Shrinkage descriptors of drying product were determined using the point set registration algorithm. Water diffusivities were further compared to those estimated by using a piecewise regression approach. Drying kinetics simulations using diffusivities obtained with the method of slopes showed an excellent agreement with experimental data, achieving moisture-averaged relative deviation values lower than 2.4%.

A simultaneous localization and mapping algorithm for sensors with low sampling rate and its application to autonomous mobile robots

In this paper we suggest a Simultaneous Localization and Mapping (SLAM) algorithm for Autonomous Mobile Robots (AMRs) which have LiDAR (light detection and ranging) type planar sensors with low sampling rate, e.g., less than 1 Hz. The proposed method uses 2-dimensional point clouds for its internal occupancy map representation and applies Point Set Registration (PSR) algorithms for mapping and localization. The approach is validated on both synthetic and real-world data. The results demonstrate that the proposed method is efficient, even when the observations are imprecise as well as the difference between consecutive measurements is high in terms of position and orientation.

A Searching Space Constrained Partial to Full Registration Approach With Applications in Airport Trolley Deployment Robot

For airports with high passenger and luggage flows, a large number of staff members have to be hired to deploy the scattered passenger luggage trolleys. To release humans from the repetitive and laborious job, we develop an autonomous trolley deployment robot to detect, transport and collect the scattered idle trolleys to recycling points. This paper will firstly illustrate the entire collection pipeline of the deployment robot system and then address the key challenge: partial to full point set registration. With the perception framework, the robot can detect the idle trolleys and acquire the pose of the trolleys on the ground, and then capture the trolley from behind, along the same direction for subsequent grasping and manipulation. With RGB-D camera and a segmentation Convolutional Neural Network, the robot can generate a partial surface point cloud of the detected trolley. The resulting point cloud, data and a pre-scanned full trolley point cloud, model, are matched by an implicit pose. To tackle the low accuracy and long computation time issues, a novel searching space-constrained point set registration algorithm is proposed to register the two overlapping point sets. Based on Branch-and-Bound (BnB) mechanism, the error between data and model is iteratively optimized. The constraint of searching space speeds up the global searching of the optimal pose, by pruning the candidate spaces which is impossible to contain the optimal result. To evaluate the performance, an airport trolley segmentation dataset and a point cloud dataset for registration are constructed. Experimental results on the datasets and synthetic dataset show that our method achieves higher accuracy and success rate than the previous methods. The experiments demonstrated in video clips validate the developed system works in real-world applications.

The Location of Component With Rectangular Pins Based on the Point Set Registration

During the production process of electronic products, the accuracy of chip component placement on printed circuit boards is crucial and directly affects the realization of product functions. This article introduces a rectangular pin part positioning method based on point set registration to obtain the exact position of the chip component during the placement process. The model point set is built from the component's datasheet. The feature points extracted from the component image are filtered according to the structure feature and the reckon angle to construct a data point set. A rigid point set registration algorithm is used to solve the alignment problem between these two point sets to obtain the offset and rotation angle. Compared with the Halcon algorithm, the Samsung method, and the state-of-the-art method, the positioning results show an advantage.

Credible seed identification for large-scale structural network alignment

Structural network alignment utilizes the topological structure information to find correspondences between nodes of two networks. Researchers have proposed a line of useful algorithms which usually require a prior mapping of seeds acting as landmark points to align the rest nodes. Several seed-free algorithms are developed to solve the cold-start problem. However, existing approaches suffer high computational cost and low reliability, limiting their applications to large-scale network alignment. Moreover, there is a lack of useful metrics to quantify the credibility of seed mappings. To address these issues, we propose a credible seed identification framework and develop a metric to assess the reliability of a mapping. To tackle the cold-start problem, we employ graph embedding techniques to represent nodes by structural feature vectors in a latent space. We then leverage point set registration algorithms to match nodes algebraically and obtain an initial mapping of nodes. Besides, we propose a heuristic algorithm to improve the credibility of the initial mapping by filtering out mismatched node pairs. To tackle the computational problem in large-scale network alignment, we propose a divide-and-conquer scheme to divide large networks into smaller ones and then match them individually. It significantly improves the recall of mapping results. Finally, we conduct extensive experiments to evaluate the effectiveness and efficiency of our new approach. The results illustrate that the proposed method outperforms the state-of-the-art approaches in terms of both effectiveness and efficiency.