Measured Point Cloud Research Articles

COMPARISON AND EVALUATION OF TLSS AND MOBILE LIDAR SCANNERS FOR MULTI-SCALE 3D DOCUMENTATION OF CULTURAL HERITAGE

Abstract. Field documentation of archaeological sites and built heritage by point-cloud is very effective in yielding arbitrary 3- dimensional shapes of various objects. There are several options for methods and devices such as TLSs, Mobile scanners, and SfM-MVS-based CRP. These are based on different technology, mechanism, and algorithm. Therefore, it is difficult to compare their precision, resolution, and measurement error in the same manner under practical conditions rather than catalog specification. The authors plan and carry out a field experiment of 3D point-cloud measurement in an archaeological site to obtain sample data for comparison between methods and devices under controlled conditions. Through comparative analysis, we evaluate character and advantage, as well as comparative measurement errors between methods and devices, then argue the combined use of different methods and devices under the “Multi-scale 3D scan strategy”.

Review of Scanning and Pixel Array-Based LiDAR Point-Cloud Measurement Techniques to Capture 3D Shape or Motion

Developments in light detection and ranging (LiDAR) technology have brought innovations in three-dimensional (3D) measurement. After mechanical laser scanners were introduced in the 1990s, the speed and point density of LiDAR measurements have increased considerably with the developments in photon imagers. On the other hand, lightweight and small-size LiDAR sensors and their integrated use with other related sensors have made the use of LiDAR widespread for mapping and navigation purposes on mobile platforms. Matrix imaging LiDAR cameras and solid-state laser scanners have no or fewer moving parts for measurement, and are unaffected by vibrations. They are usually used in mobile mapping, driverless vehicle navigation, and mobile robot navigation. Pulse or phase-shift methods are used to measure the distance from the LiDAR instrument to the scan point. The measured scan point direction is determined by the orientation angles of the beam in scanners, focal length, and pixel positions in matrix viewers, and instrument-centered 3D coordinates are calculated. LiDAR tools have their own specific capabilities and limitations. Therefore, the selection of the appropriate LiDAR for any application is very important. In this study, after LiDAR principles are introduced, scanning LiDAR and pixel-based matrix imager LiDAR methods used to measure 3D point clouds are technically examined and analyzed.

基于三维模型的谷子表型特征分析研究

As one of the ancient cultivated crops in China, millet has the characteristics of high nutritional value, drought resistance and barrenness. It also plays an important role in ensuring the supply of food in our country. At present, most of the millet breeding work uses manual extraction of phenotypic information, which is laborintensive and inefficient. Therefore, the development of an automated, efficient and accurate millet phenotype detection method has practical significance for the extraction of the millet genome. In this study, a combination of sparse reconstruction based on Structure from Motion (SfM) and Patch-based Multi-View Stereo (PMVS) was used to select three different varieties of millet. A total of 81 samples of 9 samples in each period were reconstructed to obtain a 3D model of millet. The combination of conditional filtering and statistical filtering is used to remove the noise points generated during the photographing process, and finally the obtained point cloud data is used to measure the agronomic traits of millet such as plant height and leaf area. The results show that the interval angle of 5° is the best reconstruction angle of millet. The coefficient of determination R2 of point cloud measurement results and manual measurement data regression analysis is higher than 0.94, indicating that the method used for 3D reconstruction has high applicability to different millet in different periods and high-throughput measurement of millet by the method in this paper is feasible. This study provides a theoretical basis for a millet phenotypic information measurement device

Recursive spatial-temporal clustering-based target detection with millimeter-wave radar point cloud

Millimeter-wave (mmWave) radar plays a vital role in a wide range of applications such as security surveillance and environmental monitoring. This work investigates target detection with radar point cloud measurements in the slow-motion scenario. In contrast to the existing spatial domain clustering-based target detection methods, we adopt a recursive spatial-temporal clustering (STC)-based method to detect targets in the spatial and temporal domain jointly. Specifically, the points belonging to targets are obtained by clustering with a distance metric defined in the spatial-temporal domains. In addition, to ensure the feasibility of the proposed method for practical real-time implementation, a speed-up scheme that intends to reduce the computational complexity induced by clustering in both spatial and temporal dimensions is developed. We demonstrate the efficacy of the proposed recursive STC-based method through experimental mmWave radar point cloud data where multiple people walk simultaneously in an open space. The proposed method achieves decent target detection performance improvement compared to a widely-used clustering method for target detection while its computation time is negligible compared to radar data reception time.

Three-Dimensional Point Cloud Data Pre-Processing for the Multi-Source Information Fusion in Aircraft Assembly

Wing-body assembly is a key part of aircraft manufacturing, and during the process of wing assembly, the 3D point cloud data of the components are an important basis for attitude adjustment. The large amount of measured point cloud data and the obvious noise affect the quality and efficiency of the final assembly. To address this problem, research on the pre-processing method of the component point cloud data is carried out. Firstly, a feature-enhanced point cloud resampling method is proposed to preserve key features such as part contours in the resampling process. Then, a multi-scale point cloud data noise filtering method is proposed, which can effectively filter out the outliers. The experimental results show that the proposed method improves the speed and accuracy of the subsequent point cloud analysis effectively and is successfully applied to the assembly process of a large passenger aircraft, laying the foundation for high-quality assembly.

SEpi-3D: soft epipolar 3D shape measurement with an event camera for multipath elimination.

Multipath in 3D imaging happens when one pixel receives light from multiple reflections, which causes errors in the measured point cloud. In this paper, we propose the soft epipolar 3D(SEpi-3D) method to eliminate multipath in temporal space with an event camera and a laser projector. Specifically, we align the projector and event camera row onto the same epipolar plane with stereo rectification; we capture event flow synchronized with the projector frame to construct a mapping relationship between event timestamp and projector pixel; we develop a multipath eliminating method that utilizes the temporal information from the event data together with the epipolar geometry. Experiments show that the RMSE decreases by 6.55mm on average in the tested multipath scenes, and the percentage of error points decreases by 7.04%.

A Study on Partial Shape Matching between Flake Surface and Surface of Joining Material using Measured Point Cloud

石器とは，先史時代に作成・使用されていた，生活のための道具である. 出土した石器や剥片を接合して, 母岩を再現したものを石器の接合資料と呼ぶ．接合資料を発掘調査報告書に記載する場合，実測して実測図を作成するか，または3D計測した点群に基づいて図化するが，接合資料を調査・研究するには，レーザー計測に基づいた接合資料表面の点群だけでは不十分で，内部の情報を持つ完全な接合資料の三次元点群が必要になる．完全な接合資料を生成するためには，接合資料を計測して得られた表面点群と， 接合資料を構成する石器の点群とのマッチングが必要である．しかし，接合資料を計測して得られた表面点群は，一部の石器が他の石器に部分的に隠されており，表面点群と石器剥離面を表す点群とをマッチングさせる場合，石器の一部分に着目してマッチングできる手法が要求される．そこで本稿では，接合資料を計測して得られた表面点群と，石器剥離面を表す点群との部分マッチングを行う，新しい手法を提案する．本手法により，接合資料表面点群と，石器の計測点群から，接合資料を構成する石器の空間配置を復元することが可能となる．

A point cloud preprocessing method for complicated thin-walled ring parts with irregular section

Metallic W-ring is a typically complicated thin-walled ring part with irregular section (CTWRPIC). However, there is a lack of effective measurement methods to inspect its geometric quality in the forming process, which leads to the failure of finding the forming defects in time. Due to the smooth metal surface, complex section shape, small section size and annular closed overall structure, point cloud of the section profile obtained by the line laser scanners has serious noise, outliers and missing. To this problem, this article proposes a point cloud preprocessing method for CTWRPIC. In this method, historical measurement data based outliers statistics, initial-fine rigid registration and non-rigid registration of CAD model are introduced. Firstly, the optimal alignment of point cloud from the CAD model and measurement point cloud is realized by initial-fine rigid registration. Secondly, the point cloud of CAD model is transformed by non-rigid registration to approximate the measurement profile points, the transformed point cloud of CAD model is considered as the real profile. Thirdly, based on the transformed point cloud of CAD model and distance threshold, the adaptive removal of outliers and repair of missing are realized. Finally, the correction of profile point cloud noise is completed by combining the Alpha Shapes algorithm and the proposed repair method. Based on the proposed preprocessing method and a measurement platform equipped with line laser scanners, an on-machine measurement method for metallic W-ring is proposed, and experimental results demonstrate the advantage of the proposed method. Compared with the destructive inspection method, the mean value, standard deviation and root mean square of the distance deviation of the preprocessed point cloud are all within 11 μm.

Modeling the effect of automated fiber placement induced gaps based on serial layer scanned point clouds

The gap is one of the most common defects during the AFP process, which has a nonnegligible impact on the mechanical properties of laminates. This paper proposes a novel three-dimension reconstructive gap model based on the in-process serial layer scanned point clouds, which can characterizes the gap features inside the laminate and pre-determine the mechanical performance before the subsequent procedures. The measured point cloud will be transformed into eligible and processible formats and then be used to generate a finite element grid using the element-based material property assignment method. The local material properties and fiber orientation are defined by previously assigned classes and grid geometries respectively. The effect of gap size on the in-plane tensile strength and damage evolution of laminates is investigated and both the experimental and simulation results find that the ultimate strength and stiffness show a significant decrease with the increase of gap size.

A novel method for dense point cloud reconstruction and weld seam detection for tubesheet welding robot

Welding is not yet fully automated, which is mainly limited by the manual drawing of the welding model. A novel method of automatically establishing a dense point cloud model of the tubesheet and detecting the weld seam was proposed to solve the issue in this paper. The multi-sensor system was calibrated with a fast calibration method, and a laser filter algorithm was then applied to fuse the multi-sensor data. The vocabulary tree method was carried out for tubesheet image similarity analysis, followed by the Random Sample Consensus (RANSAC) algorithm used for feature matching, aiming to establish a point cloud model based on epipolar constraint, EPNP and PMVS algorithm. Finally, a weld seam detection algorithm based on voxel point cloud density was proposed to detect the weld seam in the point cloud. After comparison, the reconstruction method has better robustness than the reference. The point cloud measurement results showed that the average row and column length errors of the reconstructed point cloud were both less than 1 %, which can meet the requirements in current welding applications. And the proposed weld seam detection method can reduce the detection error rate from 20.83 % to 9.03 %.

Real-Time 6-DOF Pose Estimation of Known Geometries in Point Cloud Data

The task of tracking the pose of an object with a known geometry from point cloud measurements arises in robot perception. It calls for a solution that is both accurate and robust, and can be computed at a rate that aligns with the needs of a control system that might make decisions based on it. The Iterative Closest Point (ICP) algorithm is widely used for this purpose, but it is susceptible to failure in practical scenarios. We present a robust and efficient solution for pose-from-point cloud estimation called the Pose Lookup Method (PLuM). PLuM is a probabilistic reward-based objective function that is resilient to measurement uncertainty and clutter. Efficiency is achieved through the use of lookup tables, which substitute complex geometric operations such as raycasting used in earlier solutions. Our results show millimetre accuracy and fast pose estimation in benchmark tests using triangulated geometry models, outperforming state-of-the-art ICP-based methods. These results are extended to field robotics applications, resulting in real-time haul truck pose estimation. By utilising point clouds from a LiDAR fixed to a rope shovel, the PLuM algorithm tracks a haul truck effectively throughout the excavation load cycle at a rate of 20 Hz, matching the sensor frame rate. PLuM is straightforward to implement and provides dependable and timely solutions in demanding environments.

A Multivariate Local Descriptor Registration Method for Surface Topography Evaluation

This paper illustrates a systematical surface topography measurement and evaluation method based on a 3D optical system. Firstly, the point cloud data of the workpiece are extracted by the use of a 3D structured light measurement system, and the STEP file of the design model is converted into point cloud data. Secondly, the local measurement point cloud (LMPC) and digital model point cloud (DMPC) are registered by a multivariate local descriptor registration scheme proposed in this study. Thirdly, the surface shapes extracted from the STEP file are applied as a reference to segment the measuring point cloud. Finally, an error analysis scheme is conducted on specific functional surfaces. An experiment was conducted to analyse the flatness, cylindricity and roughness to demonstrate the effectiveness and advantage of the method. The comparison results show that the proposed method outperforms other 3D optical surface topography analysis methods.

Trajectory tracking control method of mobile robot based on AVRX operating system

In order to improve the trajectory tracking control effect of mobile robot, a mobile robot trajectory tracking control (TRC) method based on AVRX operating system is proposed. The principle of AVRX operating system is analyzed and the hardware structure of mobile robot is designed. The input-output relationship of mobile robot trajectory is determined according to multivariable fuzzy controller and the wrong data of mobile robot trajectory tracking information is eliminated by radar point cloud measurement. The TRC model of mobile robot is designed through Euler Lagrange equation to realize the TRC of mobile robot. The experimental results show that the trajectory tracking accuracy of mobile robot is as high as 97.9%, the trajectory tracking time is 16.2 s, and the trajectory tracking error of mobile robot is only 0.04. It shows that the TRC effect of mobile robot is improved, which provides a reference for the trajectory tracking control of mobile robots.

The Potential of UAV-Acquired Photogrammetric and LiDAR-Point Clouds for Obtaining Rock Dimensions as Input Parameters for Modeling Rockfall Runout Zones

Rockfalls present a significant hazard to human activities; therefore, their identification and knowledge about potential spatial impacts are important in planning protection measures to reduce rockfall risk. Remote sensing with unmanned aerial vehicles (UAVs) has allowed for the accurate observation of slopes that are susceptible to rockfall activity via various methods and sensors with which it is possible to digitally collect information about the rockfall activity and spatial distributions. In this work, a three-dimensional (3D) reconstruction of rock deposits (width, length, and height) and their volumes are addressed, and the results are used in a rockfall trajectory simulation. Due to the availability of different sensors on the UAV, the aim was also to observe the possible differences in the dimension estimations between photogrammetric and LiDAR (light detection and ranging) point clouds, besides the most traditional method where rock deposit dimensions are measured on the field using a measuring tape. The motivation for reconstructing rock dimensions and volumes was solely for obtaining input parameters into a rockfall model. In order to study the differences between rock-measuring methods, rock dimensions were used as input parameters in a rockfall model, and additionally, modeling results such as propagation probability, maximum kinetic energies, and maximum passing heights were compared. The results show that there are no statistically significant differences between the measurement method with respect to rock dimensions and volumes and when modeling the propagation probability and maximum passing heights. On the other hand, large differences are present with maximum kinetic energies where LiDAR point cloud measurements achieved statistically significantly different results from the other two measurements. With this approach, an automated collection and measurement process of rock deposits is possible without the need for exposure to a risk of rockfall during fieldwork.

Feature Line Extraction from Building Façade Point Clouds by Exploring Spatial Topological Relationship

After using a terrestrial laser scanner to acquire building facade point clouds, the extraction of feature lines can simplify the expression of building objects, thereby contributing to the accurate construction of building facade geometric models. To address the problems of missing extraction and low accuracy in existing methods, this study proposes a feature line extraction method for building facade point clouds by exploring the different spatial topological relationship between feature and non‐feature points. The method comprises three steps: feature point extraction, feature line generation, and feature line merging and optimization. First, feature points are extracted using the convex hull distance value and relative angle, as defined in this study. Thereafter, feature points are clustered by combining the random sample consensus and region growing algorithms, and the feature lines are obtained by utilizing the iterative weighted least squares method based on the IGGIII weight function. Finally, the feature lines are merged and optimized using an endpoint search method to improve the discontinuity and missing common endpoints of the original feature lines. The experimental results obtained using simulated and measured point cloud data show that this method can accurately extract feature points, and the extracted feature lines obtained from building facade point clouds have better accuracy and completeness, which is more practical than the existing methods and can be used in many applications such as building facade measurement and urban three‐dimensional modeling.

An Active Structured Light 3D Point Cloud Measurement for Orange Shape Based On Adaptive Co-Opposite-Direction Slicing Method

An Active Structured Light 3D Point Cloud Measurement for Orange Shape Based On Adaptive Co-Opposite-Direction Slicing Method

A robotic polishing trajectory planning method for TBCs of aero-engine turbine blade using measured point cloud

PurposeThe application of thermal barrier coatings (TBCs) allows aero-engine blades to operate at higher temperatures with higher efficiency. The preparation of the TBCs increases the surface roughness of the blade, which impacts the thermal cycle life and thermal insulation performance of the coating. To reduce the surface roughness of blades, particularly the blades with small size and complex curvature, this paper aims to propose a method for industrial robot polishing trajectory planning based on on-site measuring point cloud.Design/methodology/approachThe authors propose an integrated robotic polishing trajectory planning method using point cloud processing technical. At first, the acquired point cloud is preprocessed, which includes filtering and plane segmentation algorithm, to extract the blade body point cloud. Then, the point cloud slicing algorithm and the intersection method are used to create a preliminary contact point set. Finally, the Douglas–Peucker algorithm and pose frame estimation are applied to extract the tool-tip positions and optimize the tool contact posture, respectively. The resultant trajectory is evaluated by simulation and experiment implementation.FindingsThe target points of trajectory are not evenly distributed on the blade surface but rather fluctuate with surface curvature. The simulated linear and orientation speeds of the robot end could be relatively steady over 98% of the total time within 20% reduction of the rest time. After polishing experiments, the coating roughness on the blade surface is reduced dramatically from Ra 7–8 µm to below Ra 1.0 µm. The removal of the TBCs is less than 100 mg, which is significantly less than the weight of the prepared coatings. The blade surface becomes smoothed to a mirror-like state.Originality/valueThe research on robotic polishing of aero-engine turbine blade TBCs is worthwhile. The real-time trajectory planning based on measuring point cloud can address the problem that there is no standard computer-aided drawing model and the geometry and size of the workpiece to be processed differ. The extraction and optimization of tool contact points based on point cloud features can enhance the smoothness of the robot movement, stability of the polishing speed and performance of the blade surface after polishing.

TLS and FEM combined methods for deformation analysis of tunnel structures

With the increasing of tunnel mileage, one of the most vital problems of broad-scale structures is the intelligent identification and location of structural defects, which require massive data storage capacity and high data processing efficiency. The full-field point cloud data collected by terrestrial laser scanning (TLS) can be extracted to construct three-dimensional measured models more accurately, therefore, the fusion of TLS and the finite element method (FEM) is particularly beneficial to the deformation analysis of tunnel structures. This paper combines numerical simulation and full-field measurement to interactively verify the reliability of deformation analysis based on FEM simulation and point cloud processing. The innovation of this paper is that a three-dimensional FEM is validated through the measured point cloud data of the tunnel structure. The result of this study indicated that FEM simulation and the structural health monitoring analysis are consistent.

Geometric mistuning identification and finite element model updating methods for bladed disks

Dynamic analysis results of a blisk are sensitive to inevitable geometrical mistuning, and the high-fidelity finite element model (FEM) of real blisk is of paramount importance to accurately predict structural dynamics. However, it is still challenging to accurately identify geometric mistuning and obtain a high-fidelity FEM due to the complexity and stochastic nature of geometric mistuning. In this paper, considering various types of geometrical mistuning, both a novel geometric mistuning identification and automatic finite element model updating (FEMU) method for real blisk using measured point cloud data are presented to reduce the discrepancies between the real blisk and the numerical model. The geometric surface of the real blisk is digitized to the point cloud data by a non-contact optical measurement technique. Three implementation steps of the method are introduced: 1) morphological feature detection from measured point cloud data of blisk; 2) surface nodes space mapping of FEM using isoparametric transformation; and 3) mesh shape optimization for FEMU. Eventually, two experimental cases are investigated: a simplified blisk and an industry blisk. The robustness of pre-processing algorithms for geometrical feature detection with measurement noise is also conducted. Results show that the blades with small mistuning, large deformations, and damages in the tip and leading edges are accurately identified and updated using the proposed method. Compared with measured natural frequencies for updated FEM, the maximum error is less than 3.38‰.

Precise Positioning Method of Moving Laser Point Cloud in Shield Tunnel Based on Bolt Hole Extraction

Mobile laser scanning technology used for deformation detection of shield tunnel is usually two-dimensional, which is expanded into three-dimensional (3D) through mileage, resulting in low positioning accuracy. This study proposes a 3D laser point cloud positioning method that is divided into rings in the mileage direction and blocks in the ring direction to improve the positional accuracy for shield tunnels. First, the cylindrical tunnel wall is expanded into a plane and the bolt holes are extracted using the self-adaptive parameter adjustment cloth simulation filter (CSF) algorithm combined with a density-based spatial clustering of applications with noise (DBSCAN) algorithm. Second, the mean-shift algorithm is used to obtain the center point of the bolt hole, and a model is designed to recognize the center point of different splicing blocks. Finally, the center point is combined with the standard straight-line equation to fit the straight-line positioning seam, achieving an accurate ring and block segmentation of a shield tunnel as a 3D laser point cloud. The proposed method is compared with existing methods to verify its feasibility and high accuracy using the seams located by the measured tunnel point cloud data and in the measured point cloud. The average differences between the circumferential seams positioned using the proposed method and those in the point cloud at the left waist, vault, and right waist were 3, 4, and 5 mm, respectively, and the average difference between the longitudinal seams was 3.4 mm The proposed research method provides important technical and theoretical support for tunnel safety monitoring and detection.