Improved Iterative Closest Point Research Articles

Position and orientation estimation method based on 3D digital morphology contour registration

Abstract Accurately and quickly obtaining the positions and orientations of mechanical parts based on the digital morphologies of mechanical parts are the key to achieving efficient and accurate assembly of mechanical parts. However, due to poor robustness and compactness in extracting digital morphology contours of mechanical parts, the accuracy of assembly positions and orientations obtained by using digital morphology contours cannot meet the requirements of high-precision assembly. Therefore, this paper proposes a position and orientation estimation method based on 3D digital morphology contour registration. This method extracts and optimizes digital morphology contours of mechanical parts and obtains the assembly positions and orientations by using an improved Iterative Closest Point (ICP) method to register the extracted digital morphology contours with those of the mechanical parts in assembly targets with desired positions and orientations. Experiments are conducted using mechanical parts from the ABC dataset and inertial confinement fusion micro-target. From the experimental results, when using the assembly positions and orientations obtained through the method proposed in this paper to assemble mechanical parts, it can achieve translation absolute errors of 8 μm, 5 μm, and 9 μm along the X-, Y-, and Z-axes, respectively. Similarly, the angular absolute errors in rotations around the Z-, Y-, and X-axes can be less than or equal to 0.16°, 0.15°, and 0.11°, respectively. The results prove that the proposed method in this paper exhibits high computational efficiency and accuracy, providing an effective approach for digital assembly of mechanical parts.

ICP registration with SHOT descriptor for arresters point clouds

Abstract Arresters are one of the critical components of the power system.
However, due to the arrester’s regular and uniform umbrella skirt, both traditional
manual detection methods and existing computer vision approaches exhibit limitations
in accuracy and efficiency. This paper proposes an automatic, robust, efficient arrester
point cloud registration method to address this problem. First, a robotic arm
maneuvers a depth camera to capture point cloud data from various perspectives.
Then, the fast global registration (FGR) point cloud coarse registration method
based on the signature of histograms of orientations (SHOT) descriptor to produce
preliminary registration results. This result is ultimately used as the initial value of
the improved iterative closest point (ICP) algorithm to refine the registration further.
Experimental results on various data sets collected from arrester and public data sets
show that the algorithm’s root mean square error(RMSE) is less than 0.1mm, meeting
the requirements of the engineering application of arrester detection.

Digital modelling method of coal-mine roadway based on millimeter-wave radar array

The roadway space of coal mine is narrow, and the illumination is low and uneven. Dynamic mining is accompanied by dust and water mist. The accuracy and reliability of roadway data collected by vision and laser sensors are poor. Based on this, a digital modeling method of coal mine roadway based on millimeter-wave radar array is proposed. Firstly, aiming at the problem of complex environmental interference, combined with the characteristics of small amount of single frame data of millimeter-wave point cloud, a multi-layer filtering noise reduction processing and dynamic subgraph registration method of millimeter-wave point cloud is proposed to filter out interference points and realize single radar point cloud registration. Secondly, aiming at the problem that a single millimeter-wave radar cannot scan the complete roadway information at one time, combined with the characteristics of small elevation field of view of millimeter-wave radar, a millimeter-wave radar array acquisition system is built, and an improved iterative closest point (ICP) registration algorithm based on point cloud features is established to construct the roadway point cloud fusion model. Finally, aiming at the problem of uneven and sparse point cloud after array fusion, a Poisson surface reconstruction method based on point cloud density weighted interpolation is proposed to refine the roadway structure and realize the accurate reconstruction of digital roadway model. The experimental results show that the digital modeling method of millimeter-wave radar array can accurately obtain the information of roadway surrounding rock, the density of roadway point cloud is increased by 22.4%, and the average absolute error percentage of the width and height of the reconstructed roadway model is only 0.82% and 0.72%, which provides a new research method for the reconstruction of underground roadway and an important basis for the digital modeling method of coal mine roadway.

A Method for Predicting Canopy Light Distribution in Cherry Trees Based on Fused Point Cloud Data

A proper canopy light distribution in fruit trees can improve photosynthetic efficiency, which is important for improving fruit yield and quality. Traditional methods of measuring light intensity in the canopy of fruit trees are time consuming, labor intensive and error prone. Therefore, a method for predicting canopy light distribution in cherry trees was proposed based on a three-dimensional (3D) cherry tree canopy point cloud model fused by multiple sources. First, to quickly and accurately reconstruct the 3D cherry tree point cloud model, we propose a global cherry tree alignment method based on a binocular depth camera vision system. For the point cloud data acquired by the two cameras, a RANSAC-based orb calibration method is used to externally calibrate the cameras, and the point cloud is coarsely aligned using the pose transformation matrix between the cameras. For the point cloud data collected at different stations, a coarse point cloud alignment method based on intrinsic shape signature (ISS) key points is proposed. In addition, an improved iterative closest point (ICP) algorithm based on bidirectional KD-tree is proposed to precisely align the coarse-aligned cherry tree point cloud data to achieve point cloud data fusion and obtain a complete 3D cherry tree point cloud model. Finally, to reveal the pattern between the fruit tree canopy structure and the light distribution, a GBRT-based model for predicting the cherry tree canopy light distribution is proposed based on the established 3D cherry tree point cloud model, which takes the relative projected area features, relative surface area and relative volume characteristics of the minimum bounding box of the point cloud model as inputs and the relative light intensity as output. The experiment results show that the GBRT-based model for predicting the cherry tree canopy illumination distribution has good feasibility. The coefficient of determination between the predicted value and the actual value is 0.932, and the MAPE is 0.116, and the model can provide technical support for scientific and reasonable cherry tree pruning.

Fast and accurate registration of large scene vehicle-borne laser point clouds based on road marking information

Vehicle-borne Mobile Laser Scanning (MLS) point cloud is one of the key components of 3D spatial information, which provides important geographic data support for the development of smart cities and autonomous driving. However, urban high-rise buildings will obscure the Global Navigation Satellite System (GNSS) positioning signal of the vehicle-borne MLS system, causing the problem of the inconsistent location of revisited point clouds collected in the same area, and the existing methods are still limited by low accuracy and efficiency when registering large-scale point clouds. Aiming at the complexity and timeliness of vehicle-borne point cloud registration, this paper proposes an improved Iterative Closest Point (ICP) point cloud accurate registration method based on road markings. Firstly, the road feature image is generated based on the laser reflection intensity of the ground point cloud, and the Pix2Pix_L1 (P2P_L1) transformation model is introduced to realize the identification and classification of markings. Secondly, we take road classification markings as the registration primitives, a Random Sample Consensus (RANSAC) point cloud coarse registration method based on a Fast Point Feature Histogram (FPFH) descriptor is designed, and the Chi-square distance function is used to improve the stability of corresponding points. Then, according to the normal vector characteristics of marking point clouds, the angle between the normal vectors is constrained to extract edge points of markings and select effective corresponding closest point pairs, and the translation vector is preferentially calculated according to the offset characteristics of point cloud to optimize spatial transformation matrix. Finally, a linear interpolation method is developed to correct the pose of global point clouds. Experimental results show that our method achieves high registration accuracy and computational speed on multiple datasets, and has stronger performance than the most widely used geometric feature registration methods.

Robust Map Merging Method for Collaborative LiDAR-based SLAM Using GPS Sensor

Typical lidar-based mappings need to cover the entire environment alone, which often leads to long build cycles and large computational resource requirements. Collaborative simultaneous localization and mapping have been applied to explore large and complex scenarios. However, merging local maps from multiple robots remains a challenge. It is crucial for Collaborative SLAM to identify overlapping areas and obtain the transformation matrix for map merging. To this end, this paper proposes a map merging method for collaborative lidar-based SLAM based on GPS measurements and improved Iterative Closest Point (ICP). We add a GPS sensor to the traditional robot configuration and use a simplified model to convert latitude and longitude data into plane coordinate data. According to Euclidean geometry, we determine the overlapping regions scanned by robots and calculate the initial pose transformation matrix between different robots. Based on the traditional ICP algorithm, KD-tree and normal vectors are used to register point clouds in overlapping regions accurately. The transformation matrix obtained by precise registration is used to rotate and translate the source local point cloud, so as to obtain the global point cloud map. In the real-world experiment, the initial transformation matrix is successfully obtained based on GPS measurements, which provides a good initial pose for the ICP algorithm. Compared with the ICP algorithm, the time consumption and RMSE of the improved ICP algorithm are reduced by 26.3% and 7.94%, respectively. Experimental results demonstrate that the proposed method can achieve accurate and efficient map fusion for multi-robot collaborative SLAM.

A hybrid feature-based patient-to-image registration method for robot-assisted long bone osteotomy.

The purpose of this study is to provide a simple, feasible and effective patient-to-image registration method for robot-assisted long bone osteotomy, which has rarely been systematically reported. The practical requirement is to meet the accuracy of 1mm or even higher without bone-implanted markers. A hybrid feature-based registration method termed CR-RAMSICP is proposed. Point-based coarse registration (CR) is accomplished relying on the optical retro-reflective markers attached to the tracked rigid body fixed out of the bone. In surface-based fine registration, an improved iterative closest point (ICP) algorithm based on the range-adaptive matching strategy (termed RAMSICP) is presented to cope with the robust precise matching between the asymmetric patient and image point clouds, which avoidsconverging to a local minimum. A series of registration experiments based on the isolated porcine iliums are carried out. The results illustrate that CR-RAMSICP not only significantly outperforms CR and CR-ICP in the accuracy and reproducibility, but also exhibits better robustness to the CR errors and less sensitiveness to the distribution and number of fiducial points located in the patient point cloud than CR-ICP. The proposed registration method CR-RAMSICP canstably satisfythedesiredregistration accuracy without the use of bone-implanted markers like fiducial screws. Besides, the RAMSICP algorithm used in fine registration is convenient for programming because any complex metrics or models are not involved.

A Fast Point Clouds Registration Algorithm for Laser Scanners

Point clouds registration is an important step for laser scanner data processing, and there have been numerous methods. However, the existing methods often suffer from low accuracy and low speed when registering large point clouds. To meet this challenge, an improved iterative closest point (ICP) algorithm combining random sample consensus (RANSAC) algorithm, intrinsic shape signatures (ISS), and 3D shape context (3DSC) is proposed. The proposed method firstly uses voxel grid filter for down-sampling. Next, the feature points are extracted by the ISS algorithm and described by the 3DSC. Afterwards, the ISS-3DSC features are used for rough registration with the RANSAC algorithm. Finally, the ICP algorithm is used for accurate registration. The experimental results show that the proposed algorithm has faster registration speed than the compared algorithms, while maintaining high registration accuracy.

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.

A compensation method for the eccentricity and inclination errors of spiral bevel gear based on improved ICP algorithm

Due to the manufacturing and assembly errors of the spiral bevel gear, the deviations between the measured tooth surface and its theoretical tooth surface will affect the measurement results of the tooth surface. After analyzing the tooth surface deviation of spiral bevel gear, this paper provided a compensation method to compensate the eccentricity and inclination errors by the registration of the theoretical and measured tooth surface. An improved iterative closest point (ICP) algorithm for the registration of the tooth surface was provided. The distance between the point in measured tooth surface and the nearest point in theoretical tooth surface is used to replace the minimum Euclidean distance, and the damping Gauss-Newton method is used to solve the geometric transformation matrix. Simulation experiments were carried out to verify the proposed compensation method. The compensation effect is over 93% for the concave tooth surface and over 89% for the convex tooth surface of the spiral bevel gear. The results also show that the improved ICP algorithm could compensate the eccentricity and inclination errors of the spiral bevel gear more precisely than the basic ICP algorithm.

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.

A Mobile Robotic 3-D Measurement Method Based on Point Clouds Alignment for Large-Scale Complex Surfaces

Large-scale complex surfaces are the core components of large aircraft, ships, and wind turbine generators. High-accuracy manufacturing of the components is very crucial for its performance. Automated and high-accuracy large-scale 3-D measurement is an effective means for the manufacturing accuracy control of these components. This article proposes an automated mobile robotic profile measurement method for large-scale complex surfaces, and it also proposes a high-accuracy point clouds alignment algorithm to improve the measurement accuracy. A fringe projection scanner, integrated on the end of a mobile robot, is utilized to scan the surface and obtain the local scanning point clouds. The local scanning point clouds' coarse alignment is implemented using the robot's joint data and the laser sensor's positioning data. After that, a fine alignment algorithm combining the fruit fly optimization algorithm (FOA) with the improved iterative closest point (IICP) algorithm is proposed for point clouds' fine alignment. Practical measurement was performed on a sectional wind turbine blade, and a broad region of 4.6 m ×2.6 m was measured. An accuracy evaluation method based on point-to-plane distance was adopted to evaluate the measurement error. The result that the mean distance error is less than 0.4 mm shows the proposed mobile robotic measurement system and the aligning method are of high accuracy.

Application of the Terrestrial Laser Scanning in Slope Deformation Monitoring: Taking a Highway Slope as an Example

Slope deformation monitoring is the prerequisite for disaster risk assessment and engineering control. Terrestrial laser scanning (TLS) is highly applicable to this field. Coarse registration method of point cloud based on scale-invariant feature transform (SIFT) feature points and fine registration method based on the k-dimensional tree (K-D tree) improved iterative closest point (ICP) algorithm were proposed. The results show that they were superior to other algorithms (such as speeded-up robust features (SURF) feature points, Harris feature points, and Levenberg-Marquardt (LM) improved ICP algorithm) when taking the Stanford Bunny as an example, and had high applicability in coarse and fine registration. In order to integrate the advantages of point measurement and surface measurement, an improved point cloud comparison method was proposed and the optimal model parameters were determined through model tests. A case study was conducted on the left side of the K146 + 150 point at S236 Boshan section, Shandong Province, and research results show that from 14 August 2018 and 9 November 2019, the overall deformation of the slope was small with a maximum value of 0.183 m, and the slope will continue to maintain a stable state without special inducing factors such as earthquake, heavy rainfall and artificial excavation.

BASED ON GPU FOR STRIP ADJUSTMENT ALGORITHM OF LIDAR DATA

Abstract. In order to solve the problem that the source of LiDAR data error needs to be adjusted and the data volume is large, the adjustment speed between the voyages is slow and cannot be automatically adjusted. Based on the iterative nearest point (ICP) algorithm, this paper proposes an improved iterative closest point (ICP) algorithm based on GPU parallel octree. The algorithm quickly constructs the octree of LiDAR nautical belt data in the GPU, uses the octree to quickly find the overlapping area of the nautical band, and then uses the ICP algorithm in the overlapping area to solve the adjustment parameters R and T quickly. Then the entire flight belt is quickly adjusted. Experiments with example data show that this method can quickly and automatically adjustment a large number of LiDAR data, and the adjustment precision can meet the precision requirements of the production.

Improved Iterative Closest Point(ICP) 3D point cloud registration algorithm based on point cloud filtering and adaptive fireworks for coarse registration

ABSTRACTAiming at the problem of long computation time and poor registration accuracy in the current three-dimensional point cloud registration problem, this paper presents a k-dimensional Tree(KD-tree) improved ICP algorithm(KD-tree_ICP) that combines point cloud filtering and adaptive fireworks algorithms for coarse registration. On the basis of the typical KD-tree improved ICP algorithm, the point cloud filtering process and adaptive firework coarse registration process are added. Firstly, the point cloud data acquired by the 3D laser scanner is filtered. And then the adaptive fireworks algorithm is used to perform coarse registration on the filtered point cloud data. Next, the KD-tree_ICP algorithm is used to perform accurate registration on the basis of coarse registration, and the obtained translation and rotation relations are applied to the original point cloud data to obtain the result after registration. Finally, 3D point clouds of physical models of five statues are used for experimental verification, including error analysis, stability analysis and comparison with other algorithms. The experimental results show that the method proposed in this paper has greatly improved the calculation speed and accuracy, and the algorithm is stable and reliable, which can also be applied to the reconstruction of 3D building models, restoration of cultural relics, precision machining and other fields.

A fast registration algorithm of rock point cloud based on spherical projection and feature extraction

Point cloud registration is an essential step in the process of 3D reconstruction. In this paper, a fast registration algorithm of rock mass point cloud is proposed based on the improved iterative closest point (ICP) algorithm. In our proposed algorithm, the point cloud data of single station scanner is transformed into digital images by spherical polar coordinates, then image features are extracted and edge points are removed, the features used in this algorithm is scale-invariant feature transform (SIFT). By analyzing the corresponding relationship between digital images and 3D points, the 3D feature points are extracted, from which we can search for the two-way correspondence as candidates. After the false matches are eliminated by the exhaustive search method based on random sampling, the transformation is computed via the Levenberg-Marquardt-Iterative Closest Point (LM-ICP) algorithm. Experiments on real data of rock mass show that the proposed algorithm has the similar accuracy and better registration efficiency compared with the ICP algorithm and other algorithms.

LIDAR Point Cloud Registration for Sensing and Reconstruction of Unstructured Terrain

When 3D laser scanning (LIDAR) is used for navigation of autonomous vehicles operated on unstructured terrain, it is necessary to register the acquired point cloud and accurately perform point cloud reconstruction of the terrain in time. This paper proposes a novel registration method to deal with uneven-density and high-noise of unstructured terrain point clouds. It has two steps of operation, namely initial registration and accurate registration. Multisensor data is firstly used for initial registration. An improved Iterative Closest Point (ICP) algorithm is then deployed for accurate registration. This algorithm extracts key points and builds feature descriptors based on the neighborhood normal vector, point cloud density and curvature. An adaptive threshold is introduced to accelerate iterative convergence. Experimental results are given to show that our two-step registration method can effectively solve the uneven-density and high-noise problem in registration of unstructured terrain point clouds, thereby improving the accuracy of terrain point cloud reconstruction.

Rigid blocks matching method based on contour curves and feature regions

This study proposes a blocks matching method based on contour curves and feature regions that improve the matching precision and speed with which rigid blocks with a specified thickness in point clouds are matched. The method comprises two steps: coarse matching and fine matching. In the coarse matching step, the rigid blocks are first segmented into a series of surfaces and the fracture surfaces are distinguished. Then, the contour curves of the fracture surfaces are extracted using an improved boundary growth method and the rigid blocks are coarsely matched with them. In the fine matching step, feature regions are first extracted from the fracture surfaces. Then, the centroid of each feature region is calculated and the fine matching of rigid blocks with the centroid sets is completed using an improved iterative closest point (ICP) algorithm. The improved ICP algorithm integrates the rotation angle constraint and dynamic iteration coefficient into a probability ICP algorithm, which significantly improves matching precision and speed. Experiments conducted using public blocks and Terracotta Warriors blocks indicate that the proposed method carries out rigid blocks matching more accurately and rapidly than various conventional methods.

一种聚类改进的迭代最近点配准算法

一种聚类改进的迭代最近点配准算法

Registration of 3D Point Cloud of Human Body Based on the Range Images and RGB Images

The existing 3D reconstruction techniques rarely can be easily used in people's daily life, and the traditional registration algorithms have the drawback of massive calculation. In this paper it presented a registration algorithm of body point cloud based on RGB images and Range images. First, it used kinect to obtain the RGB images and Range images from different perspectives. Then it extracted the pairs of 2D feature points on RGB images using scale-invariant feature transform (SIFT) and random sample consensus (RANSAC) algorithm application for the coarse registration and used the improved iterative closest point (ICP) algorithm for the fine registration. Second, it eliminated the background information and the noise points of the model edges. Finally it completed the registration process. Experimental results show that the algorithm can accurately accomplish the body point clouds registration using the low-cost instrument and has a relatively high efficiency.