Laser Scanning Measurements Research Articles

Digital improvements in the design and construction process of classical Chinese garden rockeries: a study based on material digitization

Rockeries have a complex and significant role in classical Chinese garden designs. They present distinct artistic characteristics and spatial hierarchies and are crucial to garden heritage conservation. Craftsmanship in rockery construction is a significant part of China’s intangible cultural heritage. Rockeries are primarily composed of naturally occurring rocks chosen for their uniqueness and complex shapes and textures. These rocks present challenges as nonstandard elements within the traditional Chinese garden context, as it is not easy to depict them using conventional blueprints and models. This complicates the design, adjustment, display, and construction of rockeries, which lacks tangible bases for reference. Consequently, the preservation and restoration of garden rockeries is difficult, and the perpetuation and dissemination of rockery construction skills face numerous challenges. This study introduces a method that combines laser scanning and photographic measurements to digitize precisely nonstandard elements of rockery stones. This approach presents an innovative design and construction workflow for rockeries by refining design processes, showcasing real effects, and resolving assembly issues. The results demonstrate that the combination of three-dimensional laser scanning and close-range photogrammetry can accurately replicate the complex forms and textures of these nonstandard elements. The stone coding and digital management system devised based on the logic of construction effectively satisfies the design and building requirements of rockeries. Correspondingly, the proposed digital construction workflow enhances the accuracy of rockery design, presentation, and evaluation, thereby contributing to the protection and restoration of rockery heritage sites and the transmission of rockery construction techniques.

An enhanced centerline extraction algorithm for complex stripes in linear laser scanning measurement

Centerline extraction is a basic and critical step for linear laser scanning measurement. However, in practical measurement, the inconsistent reflectivity of the measured surface and the external noise interference can result in complex variation of the stripe pattern, which will influence the centerline extraction accuracy of the stripe pattern and hence the measurement accuracy of the laser scanner. To solve this problem, an enhanced centerline extraction algorithm for complex laser stripes is proposed. In the preprocessing of the stripe pattern, a region separation strategy is employed to mitigate the effects of complex variations, and multi-region self-adaptive convolution is adopted to enhance the stripe pattern quality. For high-precision extraction of the stripe centerline, different convolution kernels are applied to compute the Hessian matrix for the stripe patterns in different regions to determine the normal direction of the laser line. The second order Taylor expansion is performed along this direction to work with the denoising algorithm to determine the subpixel positions of the center points on the line, and then the whole centerline of the stripe pattern is obtained by piecewise cubic Hermite interpolation methods. Experimental results show that the effectiveness of the proposed algorithm in addressing centerline extraction for complex laser stripe patterns due to stray light interference, reflective stripes, and laser stripe linewidth changes in laser scanning measurement.

A novel IoT sensor and evolution model for grinding mill liner wear monitoring

Tracking mill liner wear is essential for improved plant reliability and grinding performance. This study developed a novel IoT wear sensor and a Discrete Element Modelling coupled methodology to predict and continuously evolve shell liner’s wear pattern. The IoT sensor was purposely developed to track and report the live thickness of a shell liner. Global wear pattern was then obtained by coupling the qualitative wear intensity obtained in DEM and sensor results, from which a topological evolution model was established to generate the shell liner’s progressive wear profiles. Predictions of the wear evolution model were compared with 3D laser scan measurements collected during operation. Results indicated that the wear evolution model showed less than 8% error with laser scan measurements in quantitative wear rate comparison.

Research on the application of a dynamic programming method based on geodesic distance for path planning in Robot-Assisted laser scanning measurement of Free-Form surfaces

The use of robots equipped with 3D laser line scanners is currently one of the mainstream geometric measurement methods for free-form surfaces. The key to this technology is to plan an effective measurement path based on the geometric features of the surface. In order to improve the measurement efficiency, a new path planning method for free-form surface measurement is proposed. First, an algorithm is developed to adaptively generate sample points of the surface to be measured based on the geodesic distance. Then, the geometric length of the scanning measurement path and the amplitude variation of the Euler angle are used as the objective function, and the optimal robot scanning measurement path is generated by minimising the objective function based on a dynamic programming algorithm. Finally, experiments show that the scanning measurement path generated based on the proposed method can effectively improve the measurement efficiency compared with existing research methods.

Integration of Terrestrial Laser Scanning and field measurements data for tree stem volume estimation: Exploring parametric and non-parametric modeling approaches

Integration of Terrestrial Laser Scanning and field measurements data for tree stem volume estimation: Exploring parametric and non-parametric modeling approaches

Practical Aspects of Using Modern Laser Scanning Techniques for Measuring Mine Excavations

Abstract For more than a dozen or so years now, there has been growing interest in the use of modern laser scanning measurement methods in numerous mining operations engaged in underground excavation. However, the simple possession of a scanner does not guarantee satisfactory measurement results. This study sets out the results of scanning mine excavations in an active mine and describes the current guidelines on various aspects of the measurement process. These guidelines were developed on the basis of several hundred measurements carried out over the last dozen or so years. This study also outlines the typical measurements errors observed over the course of many years. These errors, resulting partly from hardware limitations and partly from human error when planning or actually performing measurements, were an important factor behind the introduction of standards regulating underground measurements. This study discusses in detail not only scanning that utilises traditional stationary laser scanners but also scanning based on mobile scanners. It also presents possible areas of future technological development in line with global trends.

Tree growth potential and its relationship with soil moisture conditions across a heterogeneous boreal forest landscape

Forest growth varies across landscapes due to the intricate relationships between various environmental drivers and forest management. In this study, we analysed the variation of tree growth potential across a landscape scale and its relation to soil moisture. We hypothesised that soil moisture conditions drive landscape-level variation in site quality and that intermediate soil moisture conditions demonstrate the highest potential forest production. We used an age-independent difference model to estimate site quality in terms of maximum achievable tree height by measuring the relative change in Lorey’s mean height for a five year period across 337 plots within a 68 km2 boreal landscape. We achieved wall-to-wall estimates of site quality by extrapolating the modelled relationship using repeated airborne laser scanning data collected in connection to the field surveys. We found a clear decrease in site quality under the highest soil moisture conditions. However, intermediate soil moisture conditions did not demonstrate clear site quality differences; this is most likely a result of the nature of the modelled soil moisture conditions and limitations connected to the site quality estimation. There was considerable unexplained variation in the modelled site quality both on the plot and landscape levels. We successfully demonstrated that there is a significant relationship between soil moisture conditions and site quality despite limitations associated with a short study period in a low productive region and the precision of airborne laser scanning measurements of mean height.

SAR deep learning sea ice retrieval trained with airborne laser scanner measurements from the MOSAiC expedition

Abstract. Automated sea ice charting from synthetic aperture radar (SAR) has been researched for more than a decade, and we are still not close to unlocking the full potential of automated solutions in terms of resolution and accuracy. The central complications arise from ground truth data not being readily available in the polar regions. In this paper, we build a data set from 20 near-coincident x-band SAR acquisitions and as many airborne laser scanner (ALS) measurements from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC), between October and May. This data set is then used to assess the accuracy and robustness of five machine-learning-based approaches by deriving classes from the freeboard, surface roughness (standard deviation at 0.5 m correlation length) and reflectance. It is shown that there is only a weak correlation between the radar backscatter and the sea ice topography. Accuracies between 44 % and 66 % and robustness between 71 % and 83 % give a realistic insight into the performance of modern convolutional neural network architectures across a range of ice conditions over 8 months. It also marks the first time algorithms have been trained entirely with labels from coincident measurements, allowing for a probabilistic class retrieval. The results show that segmentation models able to learn from the class distribution perform significantly better than pixel-wise classification approaches by nearly 20 % accuracy on average.

Accuracy comparison of terrestrial and airborne laser scanning and manual measurements for stem curve-based growth measurements of individual trees

Monitoring forest growth accurately is important for assessing and controlling forest carbon stocks that impact, for example, the atmospheric CO2 concentration and, consequently, the climate change. In prior studies, forest growth monitoring with laser scanning methods has resulted in relatively high errors. However, the contribution of reference measurement error to uncertainty in growth resolution has rarely been analysed, and the reference measurements are usually considered mostly flawless. In this study, a seven-year-long growth of individual trees was estimated using both airborne and terrestrial laser scanning (ALS, TLS) that have emerged as potential candidates for digital forest reference measurements. The growth values were derived for diameter at breast height (DBH) and stem volume between the years 2014 and 2021 using an indirect approach. The values obtained with laser scanning were paired with manual field measurements and also with each other to study pairwise errors. The pairwise comparison showed that even though all the three measurement methods produced good Pearson correlation coefficients for one-time measurements (all above 0.88), the coefficients for growth measurements were significantly lower (0.19–0.44 for DBH and 0.47–0.66 for stem volume). The best correlation and root mean squared deviation (RMSD) for DBH growth (ρ = 0.44, RMSD = 0.98 cm) and stem volume growth (ρ = 0.66, RMSD = 0.052 m3) was observed between the manual field measurements and the ALS-based growth measurement method, in which the tree stem curve was obtained from the 2021 point cloud, and the stem curve was predicted backwards for the year 2014 according to height growth. The ALS method suffered less from outlying values than the TLS-based growth measurement method, in which the growth was computed based on the difference of stem curves derived separately for the years 2014 and 2021. The study showed that observing the stem curve is a potential method for short-period growth monitoring. Using the pairwise comparison results, we further derived estimates for the mean and standard deviation of measurement error of each individual measurement method. For the manual measurements, the standard deviation of error was found to be approximately 0.4 cm for DBH growth and 0.03 m3 for volume growth, which were the lowest of the three methods but not by a large margin. This highlights the need for more accurate reference data as the accuracy of laser scanning-based growth estimation methods continues to approach the accuracy of manual measurements.

Methodology of Spatial Data Acquisition and Development of High-Definition Map for Autonomous Vehicles – Case Study from Wrocław, Poland

Autonomous drive systems are a dynamically developed sector of the automotive industry. The key problem in such technological solutions is to provide a reliable navigation system, which is typically based on high-definition (HD) maps supporting the identification of the position of a maneuvering vehicle. HD maps should include possibly up-to-date and detailed information on traffic lanes and on the traffic rules and regulations on such lanes. An effective development of an HD map should be based on the geodetic measurement methods, which ensure efficient and accurate acquisition of spatial data. This article presents the results of an experiment consisting in the manipulation of data obtained with the use of the mobile laser scanning method and further in employing this data in the development of an HD map in an open-source environment. The applied measurement technology and the processing method allowed data of high resolution (frequently above 1000 points per m2) and of high accuracy (3D accuracy down to less than 5 cm). The obtained data were processed in the Vector Map Builder environment (which is accessible from the level of an internet browser) and the final product - HD map was created in the Lanelet2 open-source environment. The above-described experiments allowed two main conclusions. Most importantly, they demonstrate the importance of planning and performing in-field mobile laser scanning measurements. They also point to the important role of the human analyst who needs to manually vectorize the key elements of road infrastructure and to define traffic rules.

Semantic segmentation of raw multispectral laser scanning data from urban environments with deep neural networks

Real-time semantic segmentation of point clouds has increasing importance in applications related to 3D city modelling and mapping, automated inventory of forests, autonomous driving and mobile robotics. Current state-of-the-art point cloud semantic segmentation methods rely heavily on the availability of 3D laser scanning data. This is problematic in regards of low-latency, real-time applications that use data from high-precision mobile laser scanners, as those are typically 2D line scanning devices. In this study, we experiment with real-time semantic segmentation of high-density multispectral point clouds collected from 2D line scanners in urban environments using encoder - decoder convolutional neural network architectures. We introduce a rasterized multi-scan input format that can be constructed exclusively from the raw (non-georeferenced profiles) 2D laser scanner measurement stream without odometry information. In addition, we investigate the impact of multispectral data on the segmentation accuracy. The dataset used for training, validation and testing was collected with multispectral FGI AkhkaR4-DW backpack laser scanning system operating at the wavelengths of 905 nm and 1550 nm, and consists in total of 228 million points (39 583 scans). The data was divided into 13 classes that represent various targets in urban environments. The results show that the increased spatial context of the multi-scan format improves the segmentation performance on the single-wavelength lidar dataset from 45.4 mIoU (a single scan) to 62.1 mIoU (24 consecutive scans). In the multispectral point cloud experiments we achieved a 71 % and 28 % relative increase in the segmentation mIoU (43.5 mIoU) as compared to the purely single-wavelength reference experiments, in which we achieved 25.4 mIoU (905 nm) and 34.1 mIoU (1550 nm). Our findings show that it is possible to semantically segment 2D line scanner data with good results by combining consecutive scans without the need for odometry information. The results also serve as motivation for developing multispectral mobile laser scanning systems that can be used in challenging urban surveys.

Research on rapid detection of characteristic parameters of aeroengine blade surface based on laser scanning

How to detect and evaluate the profile quality of aeroengine blades and provide technical support for blade manufacturing has become one of the key technical problems in the field of aviation manufacturing quickly and effectively. This paper studies the evaluation criteria of medium surface quality in the processing and manufacturing process of aeroengine blades, optimizes and innovates the fast extraction algorithms of various geometric parameters such as aeroengine blade section line, middle arc, front/rear edge center, front/rear edge radius, blade chord length, and chord angle, and establishes the evaluation criteria of aeroengine blade surface. According to the geometric relationship of blade chord length and front/rear edge radius in the blade section profile, the parameters are solved one by one through an optimization algorithm, combined with non-contact laser scanning measurement, and the blade detection efficiency is improved by more than 40%.

Future of precision manufacturing: Integrating advanced metrology and intelligent monitoring for process optimization

Precision manufacturing is undergoing a transformative evolution fueled by the integration of advanced metrology techniques and intelligent monitoring systems. This abstract explores the future trajectory of precision manufacturing through the convergence of these technologies, focusing on their synergistic role in process optimization. Advanced metrology techniques, including high-resolution imaging, laser scanning, and non-contact surface measurement, provide unprecedented levels of accuracy and detail in capturing dimensional data. These techniques enable manufacturers to precisely analyze component geometry, surface finish, and tolerances, facilitating the production of parts with exceptional precision and quality. Moreover, the integration of metrology within the manufacturing process allows for real-time feedback, enabling rapid adjustments and corrections to ensure adherence to design specifications. Intelligent monitoring systems complement advanced metrology by continuously collecting data from various sensors embedded within manufacturing equipment. These systems utilize artificial intelligence (AI) and machine learning algorithms to analyze vast amounts of data in real-time, detecting anomalies, predicting equipment failures, and optimizing process parameters. By leveraging data-driven insights, manufacturers can enhance production efficiency, minimize downtime, and reduce scrap rates. The synergy between advanced metrology and intelligent monitoring extends beyond quality control to encompass holistic process optimization. Through the seamless integration of these technologies, manufacturers can achieve unparalleled levels of precision, efficiency, and flexibility in their operations. For instance, real-time metrology feedback combined with AI-driven monitoring enables adaptive manufacturing processes that dynamically adjust parameters based on changing environmental conditions or material properties. Furthermore, the future of precision manufacturing lies in the adoption of a digital twin approach, where virtual replicas of physical manufacturing systems are created and synchronized with real-time data. This enables predictive maintenance, virtual prototyping, and simulation-based optimization, leading to significant cost savings and accelerated innovation cycles. The future of precision manufacturing hinges on the integration of advanced metrology and intelligent monitoring technologies. By harnessing the synergies between these innovations, manufacturers can achieve unprecedented levels of precision, efficiency, and agility, driving forward the evolution of manufacturing in the digital era.

Evaluation of mobile laser scanning acquisition scenarios for automated wood volume estimation in a temperate hardwood forest using quantitative structural models

This study explores how data from a handheld mobile laser scanning (MLS) system and quantitative structural models (QSM) can be used to estimate tree structural attributes. Four MLS acquisition scenarios were investigated in a 1 ha temperate hardwood stand, including 15 and 35 m parallel lines, nine circular plots, and a 20 m × 20 m grid. Results were compared against terrestrial laser scanning and destructive field measurements. All acquisition scenarios yielded comparable results, except for the 35 m scenario, which showed greater variability. The 20 m × 20 m grid scenario showed the highest accuracy, with an RMSE of 0.41 m (2.07%) for tree height, 3.98 cm (14.93%) for diameter at breast height, 0.21 m³ (19.28%) for merchantable wood volume, and 0.07 m³ (10.11%) for merchantable stem volume. A bias < 5% was observed for these key attributes, except for an 11.68% bias in merchantable wood volume. Overestimation of branch volume was identified as the primary source of bias related to merchantable wood volume. This study highlights MLS’s potential for accurate, non-destructive estimation of tree structural attributes, while pointing out the need to refine noise removal and to assess the most suitable acquisition scenarios for various forest types.

ACCURATE 3D MODEL OF VENICE: PRESERVING HISTORICAL DATA AND INTRODUCING SLAM IMMS FOR CHANGE DETECTION AND UPDATING PROCEDURES

Abstract. The Municipality of Venice, through Insula srl (Insula, 2024), started the RAMSES (Rilievo Altimetrico, Modellazione Spaziale E Scansione 3D) project in 2005 with the unprecedented intention of conducting a static laser scanner survey of an entire city. The authors of this paper, who have been involved in the project on behalf of the client, including drafting general contract technical specifications, wished to revisit the survey’s findings nearly two decades later. This contribution illustrates the procedures implemented to guarantee the future accessibility of the surveyed data. It is interesting to highlight how the detailed technical specifications outlined in the general technical contract section have facilitated the retrieval of the historical three-dimensional laser scanner measurements archive. Tests have been conducted to determine how the existing mobile mapping technologies may be utilised to update the three-dimensional historical data obtained in the Ramses project efficiently. Furthermore, the paper describes the surveying approach that has never been adequately described in the literature. The surveying and geo-referencing methodologies continue to have several interesting and relevant aspects, especially regarding how the topographic network has been implemented. The Ramses three-dimensional model represents an extraordinary, valuable digital archive containing portraits of the city’s conditions at the time of the mapping. Ramses 3D model, when enriched with field activities conducted using more updated technologies, can provide interesting and unique evaluations of the evolution of Venice’s landscape.

Adaptive Ant Colony Optimization with Sub-Population and Fuzzy Logic for 3D Laser Scanning Path Planning.

For the precise measurement of complex surfaces, determining the position, direction, and path of a laser sensor probe is crucial before obtaining exact measurements. Accurate surface measurement hinges on modifying the overtures of a laser sensor and planning the scan path of the point laser displacement sensor probe to optimize the alignment of its measurement velocity and accuracy. This manuscript proposes a 3D surface laser scanning path planning technique that utilizes adaptive ant colony optimization with sub-population and fuzzy logic (SFACO), which involves the consideration of the measurement point layout, probe attitude, and path planning. Firstly, this study is based on a four-coordinate measuring machine paired with a point laser displacement sensor probe. The laser scanning four-coordinate measuring instrument is used to establish a coordinate system, and the relationship between them is transformed. The readings of each axis of the object being measured under the normal measuring attitude are then reversed through the coordinate system transformation, thus resulting in the optimal measuring attitude. The nominal distance matrix, which demonstrates the significance of the optimal measuring attitude, is then created based on the readings of all the points to be measured. Subsequently, a fuzzy ACO algorithm that integrates multiple swarm adaptive and dynamic domain structures is suggested to enhance the algorithm's performance by refining and utilizing multiple swarm adaptive and fuzzy operators. The efficacy of the algorithm is verified through experiments with 13 popular TSP benchmark datasets, thereby demonstrating the complexity of the SFACO approach. Ultimately, the path planning problem of surface 3D laser scanning measurement is addressed by employing the proposed SFACO algorithm in conjunction with a nominal distance matrix.

Terrestrial Laser Scanning for Fast Spatially Resolved Cleanliness Assessment of Heliostat Fields

This paper presents a novel method for the assessment of cleanliness levels (i.e. specular relative reflectance) in the solar field by use of laser scanning. The detected backscattered laser intensity caused by soil and dust, on the otherwise specular mirror surfaces, can be used for determining the cleanliness of mirrors in the solar field with an excellent spatial resolution and acquisition speed. The backscatter behavior of soiled mirrors with distance and incident angle is analyzed and used for transferring the raw measurements values to a reference distance and incident angle. The experimental results of the novel laser scanning measurement technique as well as new insights on soiling behavior are presented. First, the calibration measurements with artificially soiled mirror samples are analyzed, followed by an extensive measurement campaign in a heliostat field. A correlation for the backscatter power and the cleanliness is found, and the first spatially resolved cleanliness measurement for a heliostat field is shown, successfully validating the measurement principle.

Particle tracking in snow avalanches with in situ calibrated inertial measurement units

Abstract In the course of an artificially triggered avalanche, a particle tracking procedure is combined with supplementary measurements, including Global Navigation Satellite System (GNSS) positioning, terrestrial laser scanning and Doppler radar measurements. Specifically, an intertial measurement unit is mounted inside a rigid sphere, which is placed in the avalanche track. The sphere is entrained by the moving snow, recording translational accelerations, angular velocities and the flux density of Earth's magnetic field. Based on the recorded data, we present a threefold analysis: (i) a qualitative data interpretation, identifying different particle motion phases which are associated with corresponding flow regimes, (ii) a quantitative time integration algorithm, determining the corresponding particle trajectory and associated velocities on the basis of standard sensor calibration, and (iii) an improved quantitative evaluation relying on a novel in situ sensor calibration technique, which is motivated by the limitations of the given dataset. The final results, i.e. the evolution of the angular orientation of the sensor unit, translational and rotational velocities and estimates of the sensor trajectory, are assessed with respect to their reliability and relevance for avalanche dynamics as well as for future design of experiments.

Measurement method of hot forging dimension based on green laser depth information.

The application field of large forgings is extensive. Accurate dimensional measurement is an important factor to ensure the quality of the finished forging product when it is forged at high temperature. Therefore, this paper proposes a green laser scanning measurement method based on depth information. First, a geometric measurement model based on depth information is established by studying the relationship between green laser depth information and forging dimension. Then, based on the heat transfer theory, distribution of the temperature field around hot forgings is studied, and an error function caused by light refraction is established using a ray tracing algorithm. After that, the error function is used to modify the measurement model to obtain the accurate distance between the forging and background plane and then obtain the dimensional information of the forging. Finally, this measurement method was experimentally verified in the laboratory, and the experimental results show that the measurement error of this method meets the dimensional measurement requirements of large hot forgings.

Generating fuel consumption maps on prescribed fire experiments from airborne laser scanning

Background Characterisation of fuel consumption provides critical insights into fire behaviour, effects, and emissions. Stand-replacing prescribed fire experiments in central Utah offered an opportunity to generate consumption estimates in coordination with other research efforts. Aims We sought to generate fuel consumption maps using pre- and post-fire airborne laser scanning (ALS) and ground measurements and to test the spatial transferability of the ALS-derived fuel models. Methods Using random forest (RF), we empirically modelled fuel load and estimated consumption from pre- and post-fire differences. We used cross-validation to assess RF model performance and test spatial transferability. Key results Consumption estimates for overstory fuels were more precise and accurate than for subcanopy fuels. Transferring RF models to provide consumption estimates in areas without ground training data resulted in loss of precision and accuracy. Conclusions Fuel consumption maps were produced and are available for researchers who collected coincident fire behaviour, effects, and emissions data. The precision and accuracy of these data vary by fuel type. Transferability of the models to novel areas depends on the user’s tolerance for error. Implications This study fills a critical need in the broader set of research efforts linking fire behaviour, effects, and emissions.