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Terrestrial laser scanning (TLS) point clouds have been widely used in deformation measurement for structures. However, reliability and accuracy of resulting deformation estimation strongly depends on quality of each step of a workflow, which are not fully addressed. This study aims to give insight error of these steps, and results of the study would be guidelines for a practical community to either develop a new workflow or refine an existing one of deformation estimation based on TLS point clouds. Thus, the main contributions of the paper are investigating point cloud registration error affecting resulting deformation estimation, identifying an appropriate segmentation method used to extract data points of a deformed surface, investigating a methodology to determine an un-deformed or a reference surface for estimating deformation, and proposing a methodology to minimize the impact of outlier, noisy data and/or mixed pixels on deformation estimation.

In practice, the quality of data point clouds and of surface extraction strongly impacts on resulting deformation estimation based on laser scanning point clouds, which can cause an incorrect decision on the state of the structure if uncertainty is available. In an effort to have more comprehensive insight into those impacts, this study addresses four issues: data errors due to data registration from multiple scanning stations (Issue 1), methods used to extract point clouds of structure surfaces (Issue 2), selection of the reference surface S_ref to measure deformation (Issue 3), and available outlier and/or mixed pixels (Issue 4). This investigation demonstrates through estimating deformation of the bridge abutment, building and an oil storage tank.

The study shows that both random sample consensus (RANSAC) and region growing–based methods [a cell-based/voxel-based region growing (CRG/VRG)] can be extracted data points of surfaces, but RANSAC is only applicable for a primary primitive surface (e.g. a plane in this study) subjected to a small deformation (case study 2 and 3) and cannot eliminate mixed pixels. On another hand, CRG and VRG impose a suitable method applied for deformed, free-form surfaces. In addition, in practice, a reference surface of a structure is mostly not available. The use of a fitting plane based on a point cloud of a current surface would cause unrealistic and inaccurate deformation because outlier data points and data points of damaged areas affect an accuracy of the fitting plane. This study would recommend the use of a reference surface determined based on a design concept/specification. A smoothing method with a spatial interval can be effectively minimize, negative impact of outlier, noisy data and/or mixed pixels on deformation estimation.

Due to difficulty in logistics, an independent measurement cannot be established to assess the deformation accuracy based on TLS data point cloud in the case studies of this research. However, common laser scanners using the time-of-flight or phase-shift principle provide point clouds with accuracy in the order of 1–6 mm, while the point clouds of triangulation scanners have sub-millimetre accuracy.

This study aims to give insight error of these steps, and the results of the study would be guidelines for a practical community to either develop a new workflow or refine an existing one of deformation estimation based on TLS point clouds.

The results of this study would provide guidelines for a practical community to either develop a new workflow or refine an existing one of deformation estimation based on TLS point clouds. A low-cost method can be applied for deformation analysis of the structure.

Although a large amount of the studies used laser scanning to measure structure deformation in the last two decades, the methods mainly applied were to measure change between two states (or epochs) of the structure surface and focused on quantifying deformation-based TLS point clouds. Those studies proved that a laser scanner could be an alternative unit to acquire spatial information for deformation monitoring. However, there are still challenges in establishing an appropriate procedure to collect a high quality of point clouds and develop methods to interpret the point clouds to obtain reliable and accurate deformation, when uncertainty, including data quality and reference information, is available. Therefore, this study demonstrates the impact of data quality in a term of point cloud registration error, selected methods for extracting point clouds of surfaces, identifying reference information, and available outlier, noisy data and/or mixed pixels on deformation estimation.

Construction schedule delays and quality problems caused by construction errors are common in the field of prefabricated buildings. The effective monitoring of the construction project process is one of the key factors for the success of a project. How to effectively monitor the construction process of prefabricated building construction projects is an urgent problem to be solved. Aiming at the problems existing in the monitoring of the construction process of prefabricated buildings, this paper proposes a monitoring method based on the feature extraction of point cloud model.

This paper uses Trimble X7 3D laser scanner to complete field data collection experiments. The point cloud data are preprocessed, and the prefabricated component segmentation and geometric feature measurement are completed based on the PCL platform. Aiming at the problem of noisy points and large amount of data in the original point cloud data, the preprocessing is completed through the steps of constructing topological relations, thinning, and denoising. According to the spatial position relationship and geometric characteristics of prefabricated frame structure, the segmentation algorithm flow is designed in this paper. By processing the point cloud data of single column and beam members, the quality of precast column and beam members is measured. The as-built model and as-designed model are compared to realize the visual monitoring of construction progress.

The experimental results show that the dimensional measurement accuracy of beam and column proposed in this paper is more than 95%. This method can effectively detect the quality of prefabricated components. In the aspect of progress monitoring, the visualization of real-time progress monitoring is realized.

This paper proposed a new monitoring method based on feature extraction of the point cloud model, combined with three-dimensional laser scanning technology. This method allows for accurate monitoring of the construction process, rapid detection of construction information, and timely detection of construction quality errors and progress delays. The treatment process based on point cloud data has strong applicability, and the real-time point cloud data transfer treatment can guarantee the timeliness of monitoring.

The purpose of this paper is to summarize the existing point cloud target detection algorithms based on deep learning, and provide reference for researchers in related fields. In recent years, with its outstanding performance in target detection of 2D images, deep learning technology has been applied in light detection and ranging (LiDAR) point cloud data to improve the automation and intelligence level of target detection. However, there are still some difficulties and room for improvement in target detection from the 3D point cloud. In this paper, the vehicle LiDAR target detection method is chosen as the research subject.

Firstly, the challenges of applying deep learning to point cloud target detection are described; secondly, solutions in relevant research are combed in response to the above challenges. The currently popular target detection methods are classified, among which some are compared with illustrate advantages and disadvantages. Moreover, approaches to improve the accuracy of network target detection are introduced.

Finally, this paper also summarizes the shortcomings of existing methods and signals the prospective development trend.

This paper introduces some existing point cloud target detection methods based on deep learning, which can be applied to a driverless, digital map, traffic monitoring and other fields, and provides a reference for researchers in related fields.

On-orbit service technology is one of the key technologies of space manipulation activities such as spacecraft life extension, fault spacecraft capture, on-orbit debris removal and so on. It is known that the failure satellites, space debris and enemy spacecrafts in space are almost all non-cooperative targets. Relatively accurate pose estimation is critical to spatial operations, but also a recognized technical difficulty because of the undefined prior information of non-cooperative targets. With the rapid development of laser radar, the application of laser scanning equipment is increasing in the measurement of non-cooperative targets. It is necessary to research a new pose estimation method for non-cooperative targets based on 3D point cloud. The paper aims to discuss these issues.

In this paper, a method based on the inherent characteristics of a spacecraft is proposed for estimating the pose (position and attitude) of the spatial non-cooperative target. First, we need to preprocess the obtained point cloud to reduce noise and improve the quality of data. Second, according to the features of the satellite, a recognition system used for non-cooperative measurement is designed. The components which are common in the configuration of satellite are chosen as the recognized object. Finally, based on the identified object, the ICP algorithm is used to calculate the pose between two frames of point cloud in different times to finish pose estimation.

The new method enhances the matching speed and improves the accuracy of pose estimation compared with traditional methods by reducing the number of matching points. The recognition of components on non-cooperative spacecraft directly contributes to the space docking, on-orbit capture and relative navigation.

Limited to the measurement distance of the laser radar, this paper considers the pose estimation for non-cooperative spacecraft in the close range.

The pose estimation method for non-cooperative spacecraft in this paper is mainly applied to close proximity space operations such as final rendezvous phase of spacecraft or ultra-close approaching phase of target capture. The system can recognize components needed to be capture and provide the relative pose of non-cooperative spacecraft. The method in this paper is more robust compared with the traditional single component recognition method and overall matching method when scanning of laser radar is not complete or the components are blocked.

This paper introduces a new pose estimation method for non-cooperative spacecraft based on point cloud. The experimental results show that the proposed method can effectively identify the features of non-cooperative targets and track their position and attitude. The method is robust to the noise and greatly improves the speed of pose estimation while guarantee the accuracy.

Many metal workpieces have the characteristics of less texture, symmetry and reflectivity, which presents a challenge to existing pose estimation methods. The purpose of this paper is to propose a pose estimation method for grasping metal workpieces by industrial robots.

Dual-hypothesis robust point matching registration network (RPM-Net) is proposed to estimate pose from point cloud. The proposed method uses the Point Cloud Library (PCL) to segment workpiece point cloud from scenes and a trained-well robust point matching registration network to estimate pose through dual-hypothesis point cloud registration.

In the experiment section, an experimental platform is built, which contains a six-axis industrial robot, a binocular structured-light sensor. A data set that contains three subsets is set up on the experimental platform. After training with the emulation data set, the dual-hypothesis RPM-Net is tested on the experimental data set, and the success rates of the three real data sets are 94.0%, 92.0% and 96.0%, respectively.

The contributions are as follows: first, dual-hypothesis RPM-Net is proposed which can realize the pose estimation of discrete and less-textured metal workpieces from point cloud, and second, a method of making training data sets is proposed using only CAD models with the visualization algorithm of the PCL.

Facility management gained profound importance due to the increasing complexity of different systems and the cost of operation and maintenance. However, due to the increasing complexity of different systems, facility managers may suffer from a lack of information. The purpose of this paper is to propose a new facility management approach that links segmented assets to the vital data required for managing facilities.

Automatic point cloud segmentation is one of the most crucial processes required for modelling building facilities. In this research, laser scanning is used for point cloud acquisition. The research utilises region growing algorithm, colour-based region-growing algorithm and Euclidean cluster algorithm.

A case study is worked out to test the accuracy of the considered point cloud segmentation algorithms utilising metrics precision, recall and F-score. The results indicate that Euclidean cluster extraction and region growing algorithm revealed high accuracy for segmentation.

The research presents a comparative approach for selecting the most appropriate segmentation approach required for accurate modelling. As such, the segmented assets can be linked easily with the data required for facility management.

Laser scanning technique is used to measure and model objects using point cloud data generated laser pulses. Conventional techniques to construct 3D models are time consuming, costly and need more manpower. The purpose of this paper is to assess the 3D model of the Sultan Salahuddin Abdul Aziz Shah Mosque’s main dome using a terrestrial laser scanner.

A laser scanner works through line of sight, which indicates that multiple scans need to be taken from a different view to ensure a complete data set. Targets must spread in all directions, and targets should be placed on fixed structures and flat surfaces for the normal scan and fine scan. After the scanning operation, point cloud data from the laser scanner were cleaned and registered before a 3D model could be developed.

As a result, the reconstruction of the 3D model was successfully developed. The samples are based on the triangle dimension, curve line, horizontal dimension and vertical dimension at the dome. The standard deviation and accuracy are calculated based on the comparison of the 21 samples taken between the high-resolution and low-resolution scanning data.

There are many ways to develop the 3D model and based on this study, the less complex ways also produce the best result. The authors implement the different types of dimensions for the 3D model assessment, which have not yet been considered in the past.

The purpose of this research is to outline in detail the procedure of remote data capture using laser scanning and the subsequent processing required in order to identify a new methodology for creating full engineering drawings (orthographic and 3D models) from laser scan and image survey data for historic structures.

Historic building information modelling (HBIM) is proposed as a new system of modelling historic structures; the HBIM process begins with remote collection of survey data using a terrestrial laser scanner combined with digital cameras. A range of software programs is then used to combine the image and scan data.

Meshing of the point cloud followed by texturing from the image data creates a framework for the creation of a 3D model. Mapping of BIM objects onto the 3D surface model is the final stage in the reverse engineering process, creating full 2D and 3D models including detail behind the object's surface concerning its methods of construction and material makeup, this new process is described as HBIM.

The future research within this area will concentrate on three main stands. The initial strand is to attempt improve the application of geometric descriptive language to build complex parametric objects. The second stand is the development of a library of parametric based on historic data (from Vitruvius to 18th century architectural pattern books). Finally, while it is possible to plot parametric objects onto the laser scan data, there is need to identify intermediate software platforms to accelerate this stage within the HBIM framework.

Building information modelling (BIM) is a digital representation of the physical and functional characteristics of a building. Its use offers a range of benefits in terms of achieving the efficient design, construction, operation and maintenance of buildings. Applying BIM at the outset of a new build project should be relatively easy. However, it is often problematic to apply BIM techniques to an existing building, for example, as part of a refurbishment project or as a tool supporting the facilities management strategy, because of inadequacies in the previous management of the dataset that characterises the facility in question. These inadequacies may include information on as built geometry and materials of construction. By the application of automated retrospective data gathering for use in BIM, such problems should be largely overcome and significant benefits in terms of efficiency gains and cost savings should be achieved.

Laser scanning can be used to collect geometrical and spatial information in the form of a 3D point cloud, and this technique is already used. However, as a point cloud representation does not contain any semantic information or geometrical context, such point cloud data must refer to external sources of data, such as building specification and construction materials, to be in used in BIM.

Hyperspectral imaging techniques can be applied to provide both spectral and spatial information of scenes as a set of high-resolution images. Integrating of a 3D point cloud into hyperspectral images would enable accurate identification and classification of surface materials and would also convert the 3D representation to BIM.

This integrated approach has been applied in other areas, for example, in crop management. The transfer of this approach to facilities management and construction would improve the efficiency and automation of the data transition from building pathology to BIM. In this study, the technological feasibility and advantages of the integration of laser scanning and hyperspectral imaging (the latter not having previously been used in the construction context in its own right) is discussed, and an example of the use of a new integration technique is presented, applied for the first time in the context of buildings.

Most of the existing ground filtering algorithms are based on the Cartesian coordinate system, which is not compatible with the working principle of mobile light detection and ranging and difficult to obtain good filtering accuracy. The purpose of this paper is to improve the accuracy of ground filtering by making full use of the order information between the point and the point in the spherical coordinate.

First, the cloth simulation (CS) algorithm is modified into a sorting algorithm for scattered point clouds to obtain the adjacent relationship of the point clouds and to generate a matrix containing the adjacent information of the point cloud. Then, according to the adjacent information of the points, a projection distance comparison and local slope analysis are simultaneously performed. These results are integrated to process the point cloud details further and the algorithm is finally used to filter a point cloud in a scene from the KITTI data set.

The results show that the accuracy of KITTI point cloud sorting is 96.3% and the kappa coefficient of the ground filtering result is 0.7978. Compared with other algorithms applied to the same scene, the proposed algorithm has higher processing accuracy.

Steps of the algorithm are parallel computing, which saves time owing to the small amount of computation. In addition, the generality of the algorithm is improved and it could be used for different data sets from urban streets. However, due to the lack of point clouds from the field environment with labeled ground points, the filtering result of this algorithm in the field environment needs further study.

In this study, the point cloud neighboring information was obtained by a modified CS algorithm. The ground filtering algorithm distinguish ground points and off-ground points according to the flatness, continuity and minimality of ground points in point cloud data. In addition, it has little effect on the algorithm results if thresholds were changed.