Hand-held Laser Scanner Research Articles

Laboratory Tests of Metrological Characteristics of a Non-Repetitive Low-Cost Mobile Handheld Laser Scanner.

The popularity of mobile laser scanning systems as a surveying tool is growing among construction contractors, architects, land surveyors, and urban planners. The user-friendliness and rapid capture of precise and complete data on places and objects make them serious competitors for traditional surveying approaches. Considering the low cost and constantly improving availability of Mobile Laser Scanning (MLS), mainly handheld surveying tools, the measurement possibilities seem unlimited. We conducted a comprehensive investigation into the quality and accuracy of a point cloud generated by a recently marketed low-cost mobile surveying system, the MandEye MLS. The purpose of the study is to conduct exhaustive laboratory tests to determine the actual metrological characteristics of the device. The test facility was the surveying laboratory of the University of Agriculture in Kraków. The results of the MLS measurements (dynamic and static) were juxtaposed with a reference base, a geometric system of reference points in the laboratory, and in relation to a reference point cloud from a higher-class laser scanner: Leica ScanStation P40 TLS. The Authors verified the geometry of the point cloud, technical parameters, and data structure, as well as whether it can be used for surveying and mapping objects by assessing the point cloud density, noise and measurement errors, and detectability of objects in the cloud.

Urban tree health assessment using multifaceted remote sensing datasets: A case study in Hong Kong

Although climate change is impacting various aspects of our environment, it is important to note that the overall risk to trees remains low, especially in urban areas like Hong Kong where the benefits of trees to society are significant. The trees planted in an urban setting are isolated and have several limiting factors including, excessive run-off, urban pollution, physical damage and limited root growth, which sometimes lead for tree failure incidents. The conventional on-site tree health assessment method is time consuming thus, requiring a remote sensing based method to effectively and routinely monitor the health status of urban trees. In this study several types of remote sensing datasets have been exploited to assess the health status of more than 700 Old and Valuable Trees (OVTs) and Stone Wall Trees (SWTs) around Hong Kong. These datasets include the data from Terrestrial LiDAR (Light Detection and Ranging) Surveys (TLS), Handheld Laser Scanner (HLS), Airborne LiDAR Surveys (ALS) and airborne multispectral data. For validation purpose, the in situ tree parameters data was also obtained from the Tree Management Office (TMO) of the Greening, Landscape & Tree Management Section (GLTMS) under the Development Bureau of the Hong Kong SAR Government. The results have indicated that over the period of four years (2017–2020) there has been a decline in the health of some target trees which can be attributed to the increased infestation rate in trees and severe weather conditions. The usage of LiDAR data has supported the fact that different tree structural forms can effectively be extracted and can help making informed decisions on the precise health conditions of urban trees.

Detection and automatic identification of loess sinkholes from the perspective of LiDAR point clouds and deep learning algorithm

Nowadays, the detection and automatic identification of the three-dimensional structure of sinkholes is extremely lacking, which has resulted in significant gaps in sinkholes mapping, soil erosion estimation and morphological studies. In this study, we discovered 249 sinkholes on a river terrace (about 2050 m long and 100 m wide) in a small watershed of Chinese Loess Plateau. Subsequently, we used the unmanned aircraft systems (UAS) and handheld laser scanner (HLS) to investigate these loess sinkholes in detail. We introduced the PointNet ++ deep learning model to train the point cloud dataset for 50 epochs and then selected the best model. In order to evaluate the identification accuracy and transferability of the model, we input point clouds of the unknown prediction area into the trained model to predict the sinkhole point clouds. The trained model exhibits excellent transferability and can effectively identify the sinkhole point clouds in the predicted area (OA = 0.935, IoU (Sinkhole) = 0.662, mIoU = 0.794, AUC = 0.966, Recognition rate = 82.46 %), and even sinkholes with complex connected structures can be accurately identified. This study provides a new perspective for future large-area LiDAR surveys, mapping, and assessment of sinkholes.

Research on Quantitative Analysis Methods for the Spatial Characteristics of Traditional Villages Based on Three-Dimensional Point Cloud Data: A Case Study of Liukeng Village, Jiangxi, China

Traditional villages are important carriers of cultural heritage, and the quantitative study of their spatial characteristics is an important approach to their preservation. However, the rapid extraction, statistics, and estimation of the rich spatial characteristic indicators in these villages have become bottlenecks in traditional village research. This paper employs UAV (unmanned aerial vehicle) and handheld laser scanners to acquire three-dimensional point cloud data and construct a spatial feature three-dimensional calculation workflow of “field data collection—data processing—data analysis and application”, which enables the rapid acquisition, processing, and analysis of three-dimensional village data. Typical case studies are conducted in Liukeng Village, China, focusing on the quantification of village spatial characteristics at three levels: topography, streets and alleys, and individual buildings, as well as comparative studies of multiple villages across different regions. The quantification of three-dimensional data reveals the regularity of village spatial characteristics and uncovers the spatial wisdom embedded in the site selection and spatial structure of traditional villages. This paper establishes a complete technical route for the quantitative analysis of villages, deepens public understanding of the diverse value of traditional villages, and provides technical support for research and practice in related fields.

Contribution of Dental Dimensions in Sex Prediction and Identification

The purpose of this research is to assess the differences of tooth measurements between Bulgarians and Spaniards. The survey included 232 Bulgarians aged 20–40 years. Vestibulo-lingual and mesio-distal dental diameters were obtained by a hand-held laser scanner (FastSCAN Cobra, Polhemus Inc., Colchester VT) and analyzed with SPSS 28.0. We detected significant differences in mesio-distal measurements of upper central incisors, premolars, and molars and mandibular first molars, vestibulo-lingual measurements of upper central incisors, canines and first molars and lower first molars between Bulgarians and Spaniards. Our findings revealed that tooth measurements vary in the populations and thus they can be reliable predictors in forensic identification.

Integration of Handheld and Airborne Lidar Data for Dicranopteris Dichotoma Biomass Estimation in a Subtropical Region of Fujian Province, China

Dicranopteris dichotoma is a pioneer herbaceous plant species that is tolerant to barrenness and drought. Mapping its biomass spatial distribution is valuable for understanding its important role in reducing soil erosion and restoring ecosystems. This research selected Luodihe watershed in Changting County, Fujian Province, China, where soil erosion has been a severe problem for a long time, as a case study to explore the method to estimate biomass, including total and aboveground biomass, through the integration of field measurements, handheld laser scanning (HLS), and airborne laser scanning (ALS) data. A stepwise regression model and an allometric equation form model were used to develop biomass estimation models based on Lidar-derived variables at typical areas and at a regional scale. The results indicate that at typical areas, both total and aboveground biomass were best estimated using an allometric equation form model when HLS-derived height and density variables were extracted from a window size of 6 m × 6 m, with the coefficients of determination (R2) of 0.64 and 0.58 and relative root mean square error (rRMSE) of 28.2% and 35.8%, respectively. When connecting HLS-estimated biomass with ALS-derived variables at a regional scale, total and aboveground biomass were effectively predicted with rRMSE values of 17.68% and 17.91%, respectively. The HLS data played an important role in linking field measurements and ALS data. This research provides a valuable method to map Dicranopteris biomass distribution using ALS data when other remotely sensed data cannot effectively estimate the understory vegetation biomass. The estimated biomass spatial pattern will be helpful to understand the role of Dicranopteris in reducing soil erosion and improving the degraded ecosystem.

Accuracy of tree mapping based on hand-held laser scanning comparing leaf-on and leaf-off conditions in mixed forests

Accuracy of tree mapping based on hand-held laser scanning comparing leaf-on and leaf-off conditions in mixed forests

Comparative Study of Single-Wood Biomass Model at Plot Level Based on Multi-Source LiDAR

Forests play an important role in promoting carbon cycling and mitigating the urban heat island effect as one of the world’s major carbon storages. Scientifically quantifying tree biomass is the basis for assessing tree carbon storage and other ecosystem functions. In this study, a sample plot of Populus tomentosa plantation in the Olympic Forest Park in Beijing was selected as the research object. Point cloud data from three types of laser scanners, including terrestrial laser scanner (TLS), backpack laser scanner (BLS), and handheld laser scanner (HLS), were used to estimate the biomass of single tree trunks, branches, leaves, and aboveground total biomass based on the Allometric Biomass Model (ABM) and Advanced Quantitative Structure Model (AdQSM). The following conclusions were drawn from the estimation results: (1) For the three types of laser scanner point clouds, the biomass estimation values obtained using the AdQSM model were generally higher than those obtained using the Allometric Biomass Model. However, the estimation values obtained using the two models were similar, especially for tree trunks and total biomass. (2) For total biomass and individual biomass components of single trees, the results obtained from handheld and terrestrial laser scanner point clouds are consistent; however, they show some differences from the results obtained from backpack-mounted point clouds. This study further enriches the methodological system for estimating forest biomass, providing a theoretical basis and reference for more accurate estimates of forest biomass and more sustainable forest management.

Cultural heritage characteristics and damage analysis based on multidimensional data fusion and HBIM–taking the former residence of HSBC bank in Xiamen, China as an example

In the context of digitalizing cultural heritage, HBIM is garnering increasing attention from heritage conservationists for its advantages. In conservation practices for cultural heritage, expressing regional characteristics and documenting in HBIM has become increasingly crucial, and these needs are inextricably linked to the need for models with a high level of detail (LOD). This paper presents a combination of Terrestrial Laser Scanning surveys, UAV photogrammetry, and handheld laser scanning to scan cultural heritage. The scan data obtained from multiple digital surveys serves as the foundation for modelling HBIM. For the regional characteristics, the HBIM model was built with detailed geometric shapes, configurations, materials, and colors of each element, as well as complex patterns and damage using the parametric models and mesh models. The information of the building and its components stored in the HBIM model. Consequently, obtained a highly detailed HBIM that not only captures the intricate details of the ideal state and real-world damage and degradation with minimal error but also serves as a repository for a multitude of information types. It has been concluded that a high LOD is essential to representing the regional geometric feature of a building and recording the damage of cultural heritage.

A Coal Mine Tunnel Deformation Detection Method Using Point Cloud Data.

In recent years, the deformation detection technology for underground tunnels has played a crucial role in coal mine safety management. Currently, traditional methods such as the cross method and those employing the roof abscission layer monitoring instrument are primarily used for tunnel deformation detection in coal mines. With the advancement of photogrammetric methods, three-dimensional laser scanners have gradually become the primary method for deformation detection of coal mine tunnels. However, due to the high-risk confined spaces and distant distribution of coal mine tunnels, stationary three-dimensional laser scanning technology requires a significant amount of labor and time, posing certain operational risks. Currently, mobile laser scanning has become a popular method for coal mine tunnel deformation detection. This paper proposes a method for detecting point cloud deformation of underground coal mine tunnels based on a handheld three-dimensional laser scanner. This method utilizes SLAM laser radar to obtain complete point cloud information of the entire tunnel, while projecting the three-dimensional point cloud onto different planes to obtain the coordinates of the tunnel centerline. By using the calculated tunnel centerline, the three-dimensional point cloud data collected at different times are matched to the same coordinate system, and then the tunnel deformation parameters are analyzed separately from the global and cross-sectional perspectives. Through on-site collection of tunnel data, this paper verifies the feasibility of the algorithm and compares it with other centerline fitting and point cloud registration algorithms, demonstrating higher accuracy and meeting practical needs.

Визначення об’єму насипів методом GNSS та лазерного сканування

In the modern period, the calculation of volumes of embankments plays an important role in a number of industries, starting from construction and ending with mining. This calls for the improvement of methods and technologies to ensure accurate and operational calculations of embankment volumes. One of the promising directions in this context is the use of Triangulated Irregular Networks (TIN) and Mesh models based on data obtained during geodetic measurements. Surveying is a large amount of accurate spatial measurement data that is necessary to create detailed geometric models of objects. The obtained geodetic data allow creating TIN and Mesh models that reflect the relief of the land surface with the necessary accuracy and detail. The use of these models to calculate the volumes of embankments becomes a key element in improving the processes of design, construction and management of natural resources. The purpose of this work is to compare the accuracy of the calculation of embankment volumes based on the data obtained by the hand-held laser scanner Stonex X120GO and the GNSS receiver Stonex S700A. Method. The method of comparing equal-precision measurements was used. Since there were no reference volumes of embankments at the site of the works, the difference between the volumes calculated on the basis of the data obtained with the hand-held laser scanner Stonex X120GO and the GNSS receiver Stonex S700A was used. Also, comparative and visual analysis was used to compare the difference between Mesh models obtained on the basis of different data sources. The results. The accuracy of determining the volumes of embankments obtained by a handheld laser scanner Stonex X120GO and a GNSS receiver Stonex S700A was studied. The average error of the differences between the determined volumes was 4.62%. Point clouds and Mesh models themselves were also analyzed (number of points, triangles, surface shape). The advantages and disadvantages of using a GNSS receiver and a hand-held laser scanner to collect data for calculating the volumes of material mounds are identified. Scientific novelty and practical significance. A technique for checking the accuracy of determining the volume of embankments without known reference values is proposed. The influence of the embankment area error on the accuracy of volume determination was studied. According to the results of the study, it is possible to assert the advantages in the detail of the received data and the speed of shooting when using a manual laser scanner, and about some difficulties associated with the desired presence of a powerful computer, a large array of data, an increase in the volume of camera work in comparison with the data obtained under the time of application of the GNSS receiver and the RTK method for determining the volumes of embankments

Comparison of As-Built Surveys Using Handheld Laser Scanner and Conventional Method

With the advent of laser scanners in general, handheld laser scanners have become a tool used by surveyors to capture point clouds. The handheld laser scanner is useful for tight and confined spaces where a terrestrial laser scanner or a human cannot enter. Many surveyors still rely on the conventional method, which involves using a distometer and measuring tape for their strata as-built surveys. This is due to the simplicity and cost-effectiveness of the equipment. However, the conventional method is highly susceptible to human errors, unlike the laser scanner, which captures all features in point clouds without human intervention. In this research, data collected from both handheld laser scanners and conventional methods are compared. The handheld laser scanner captured a total of three levels. After collecting data at the site, the GeoSLAM Hub software processed the point clouds. This software helps convert the 3D point clouds into a 2D plan, with dimensions listed for the subsequent comparison process. The comparison focuses on the X and Y dimensions of the unit, with a tolerance of ±0.1m as per the circular KPUP-1-2015-JILID-I-v2016 provided by JUPEM. Following the comparison, the Root Mean Square Error (RMSE) is determined, with the lowest and highest values being 0.018m and 0.024m, respectively. In conclusion, the results show a minimum difference of 0.001m and a maximum difference of 0.049m, which falls within the provided tolerance. The handheld laser scanner offers visual aids to surveyors, enabling them to identify discrepancies' locations quickly. However, its drawback lies in the equipment's cost. Ultimately, the strata as-built plan is prepared for the three surveyed levels.

Sex-related Differences in the Orbital Area of Bulgarians – a 3D Laser Scanning

The present study aimed to provide up-to-date information on normal sex-related dimensions of the orbital region in young Bulgarian adults, using a 3D laser scanner. The three-dimensional coordinates of several soft-tissue landmarks on the orbits and face were obtained using a hand-held laser scanner (FastSCAN Cobra, Polhemus Inc, Colchester VT) in 95 healthy individuals (46 males and 49 females) of Bulgarian origin aged 21–30 years. From the landmarks outer and intercanthal widths, length of eye fissure, and height of the orbit were calculated and averaged for sex. The data were analyzed using descriptive statistics and paired t-test. Statistically significant difference was found in outer canthal width, both eyes fissure length, and orbital height between sexes. The differences in the inner canthal width did not reach statistical significance (p > 0.05). The data of the present study could serve as a quantitative characteristic of human orbital morphology. Forensic applications, evaluations of traumas, craniofacial alterations, and facial reconstruction may also benefit from the data.

Assessing the Performance of Handheld Laser Scanning for Individual Tree Mapping in an Urban Area

Precise individual tree or sample-based inventories derived from 3D point cloud data of mobile laser scanning can improve our comprehensive understanding of the structure, function, resilience, biodiversity, and ecosystem services of urban forests. This study assessed the performance of a handheld laser scanning system (HLS) for the extraction of tree position, diameter at breast height (DBH), and tree height (H) in an urban area. A total of 2083 trees of 13 species from 34 plots were analyzed. The results showed that the registration of tree positions using ground control points (GCPs) demonstrated high accuracy, with errors consistently below 0.4 m, except for a few instances. The extraction accuracy of DBH for all trees and individual species remained consistently high, with a total root mean square error (RMSE) of 2.06 cm (6.89%) and a bias of 0.62 cm (2.07%). Notably, broad-leaved trees outperformed coniferous trees, with RMSE and bias values of 1.86 cm (6%) and 0.76 cm (2.46%), respectively, compared to 2.54 cm (9.46%) and 0.23 cm (0.84%), respectively. The accuracy of H extraction varied significantly among different species, with R2 values ranging from 0.65 to 0.92. Generally, both DBH and H were underestimated compared to ground measurements. Linear mixed-effects models (LMEs) were applied to evaluate factors affecting the performance of HLS with the plot as a random factor. LME analysis revealed that plant type and terrain significantly influenced the accuracy of DBH and H derived from HLS data, while other fixed factors such as plot area, tree density, and trajectory length showed no significance. With a large sample size, we concluded that the HLS demonstrated sufficient accuracy in extracting individual tree parameters in urban forests.

ENHANCING URBAN PATHFINDING FOR PEDESTRIANS THROUGH FUSION OF MLS AND HMLS DATA

Abstract. Pedestrian pathfinding is crucial for enhancing pedestrian mobility in urban environments. In this research a method to generate navigation graphs based on Mobile Laser Scanning (MLS) and Handheld Laser Scanning (HMLS) data fusion is developed. The input data comprises a 2-kilometer urban street network that integrates both MLS and HMLS data, effectively mitigating sidewalk occlusions caused mainly by parked vehicles. The proposed method encompasses the following steps: (1) Deep Learning semantic segmentation of urban ground elements and navigation-related obstacles, (2) Static vs. Dynamic object differentiation to replicate conditions of unobstructed passage. (3) Sidewalk enrichment with inclination, preservation status, and width. (4) Physical accessibility estimation between sidewalks and crosswalks incorporating curb information. And (5) navigation graph computation based on the enhanced sidewalk data, crosswalks, with accurate node location and connections. Routes were calculated within a Geographical Information System, and results ensure that pedestrians can navigate in urban environments with precision and efficiency.

Rapid pre-typhoon safety inspection system for improving construction site resilience

Coastal cities are vulnerable to typhoon disasters due to their unique geographical location. Although significant progress has been made in the safety early warning system for construction sites, there are few studies on site safety management under typhoon disasters. Moreover, traditional manual methods of on-site emergency management are lagging behind and rigid, making it difficult to respond quickly to safety inspections manually. This study aims to automatically identify safety hazards and provide effective response measures during typhoon warnings, ultimately enhancing the safety of construction sites. Therefore, based on the MATLAB platform, this study developed a rapid safety inspection system (RSS-Typh) for the construction site during the typhoon warning period. In addition, the standard operating procedures (SOP) related to the system are formulated to assist the comprehensive safety inspection of the site before the typhoon and improve the inspection efficiency. The specific operation can be divided into three steps: 1.Using a combination of drones and handheld laser scanners to complete the complete construction site data acquisition in less than 2 h, and complete the modeling and refinement of the point cloud model in 5 h; 2.Using the developed hidden danger detection algorithm based on automatic point cloud to effectively identify the deformation-related hidden dangers at the construction site; 3.Use the designed automatic query security countermeasure GUI system to make rapid rectification. This study assists site managers in swiftly implementing effective safety management on construction sites, elevating the safety level of the site before typhoon. Furthermore, it serves to advance the automation of safety management in construction sites, promoting further developments in this field.

핸드형 및 백팩형 레이저 스캐너의 모델링 활용방안

핸드형 및 백팩형 레이저 스캐너의 모델링 활용방안

Method for Quantitative Assessment of Olecranon Fossa Using 3D Hand-held Laser Scanner in the Field of Forensic Anthropology

The study is based on a creation of virtual model of olecranon fossa through 3D visualization and its usage as a sex discriminant. The study was focused on an assessment of 135 complete humeri from 47 females and 88 males. The surface of olecranon fossa was captured in a 3D image, using a Hand-held Laser Scanner (FastSCAN). The result was a 3D shape comprising two tetrahedrons with a common apex and a common lateral face. This 3D model was determined by five landmarks: the most superior point of the olecranon fossa, the most lateral point of the olecranon fossa seen on the posterior surface, the most medial point of the olecranon fossa, the point on the inferior edge of the olecranon fossa, directly in the middle, and the deepest point into the fossa. Therefore, the volume of the virtual model was equal to the total of the volumes of these two tetrahedrons. The results were processed with SPSS 17.0 using Discriminant Function Analysis. The percentage of cases classified correctly is 92.5% according to the sex determination. This study demonstrates a 3D method which can be used successfully for sex determination, especially in case of highly fragmented bones that impede traditional anthropometric analyses.

High-Resolution Point Cloud Registration Method for Three-Dimensional Piping Measurements

In this study, we propose a method for generating highly accurate high-density point clouds of piping facilities using an unmanned aerial vehicle (UAV) laser scanner and a handheld laser scanner. The point cloud for each scanline measured by the UAV scanner is repositioned on the piping axis, and the handheld scanner’s 3D point cloud is subsequently registered so that the center axis of the piping coincides with the UAV point cloud as a reference. The method proposed in this study was used to accurately reconstruct linear piping measured in high winds, which can easily deteriorate measurement accuracy. Whereas the conventional method incurred a deviation of 44.3 mm between the predicted and true values at altitudes of 15 m, the proposed method reduced this deviation to 19.4 mm. An application of the registration method demonstrated that the combined use of the two laser scanners enabled the creation of a high-density point cloud.

Registration of Knee Articulating Surface Utilizing a Portable Handheld Laser Scanner: A Cadaveric Study

INTRODUCTION Image-based navigation systems for total knee arthroplasty (TKA) require intra-operative registration of anatomical landmarks to the pre-operative images. Unfortunately, this process is long, tedious, and involves multiple steps with an optical tracking probe to bony identify landmarks. We hypothesized that use of a handheld laser scanner can achieve an accurate and fast registration of the cartilage surface. METHODS Magnetic resonance imaging (MRI) scans were obtained for six cadavers and images were segmented to produce 3-dimenstional (3D) models of the articulating surface of the tibia and femur using commercial software (MIMICS, Materialise). A portable, hand-held laser scanner (E4D Technologies) was used to generate surface models of the same tibia and femur. An iterative closest point (ICP) algorithm was then used to align the handheld scans to the MRI scans. The accuracy of the registration fit was determined by the Root Mean Square (RMS) difference between the two surfaces. RESULTS There were a total of 72 scans. All 6 cadaveric knees were successfully registered to their MRI images with <1mm mean RMS error. The mean time for successful registration was 54 +/- 24 seconds. The inter- and intra-observer reliability were 0.92 and 0.87 respectively. CONCLUSION A handheld laser scanner can produce fast and accurate registration of the tibia and femur during a TKA. Registration was accurate within less than 1mm with multiple operators of different training. Further studies should assess in vivo accuracy of scanned bone topography and implant positioning compared to other technologies.