Laser Pointer Research Articles

The Effect of Vegetation Ecological Restoration by Integrating Multispectral Remote Sensing and Laser Point Cloud Monitoring Technology

This research aims to evaluate and monitor the effectiveness of vegetation ecological restoration by integrating Multispectral Remote Sensing (MRS) and laser point cloud (LPC) monitoring technologies. Traditional vegetation restoration monitoring methods often face challenges of inaccurate data and insufficient coverage, and the use of MRS or LPC techniques alone has its limitations. Therefore, to more accurately monitor the vegetation restoration status, this study proposes a new monitoring method that combines the advantages of the large-scale coverage of MRS technology and the high-precision three-dimensional structural data analysis capability of LPC technology. This new method was applied in the Daqing oilfield area of China, aiming to provide effective ecological restoration assessment methods through the precise monitoring and analysis of regional vegetation growth and coverage. The results showed that there was a negative correlation between the vegetation humidity index and vegetation growth in the Daqing oilfield in 2023. The estimated monitoring effect of the research method could reach over 90%, and the coverage area of hydrangea restoration in the monitoring year increased by 7509 km2. The research technology was closer to the actual coverage situation. The simulation image showed that the vegetation coverage in the area has significantly improved after returning farmland to forests. Therefore, the technical methods used can effectively monitor the ecological restoration of vegetation, which has great research significance for both vegetation restoration and monitoring.

Evaluating the Effectiveness of ICESat-2 ATL03 Data for Georeferencing Satellite Images

Abstract. ICESat-2 laser points, as a kind of public geographic data, have high ranging accuracy and have great potential in improving the geometric positioning accuracies of satellite images. This paper aims to evaluate the effectiveness of ICESat-2 ATL03 data for georeferencing satellite image. Our study combines ATL03 laser points with an image basemap processed using open-source data positioning to construct digital control photo (DCP), facilitating the rapid transformation of laser points into laser control points for image georeferencing. Through a comprehensive analysis involving comparison with traditional georeferencing methods using ground control points (GCPs), our findings indicate a significant enhancement in accuracy and efficiency in georeferencing, showcasing the potential of ICESat-2 data in improving the quality and reliability of remote sensing images. Experiments using the extracted laser points and GeoEye-1 images to construct DCP for georeferencing IKONOS images in Hobart, Australia demonstrate that the proposed method improved the horizontal accuracy from 2.33 meters to 0.97 meters and the vertical accuracy from 3.44 meters to 0.59 meters, compared to the georeferencing method without GCPs.

Filtering-Assisted Airborne Point Cloud Semantic Segmentation for Transmission Lines

Point cloud semantic segmentation is crucial for identifying and analyzing transmission lines. Due to the number of point clouds being huge, complex scenes, and unbalanced sample proportion, the mainstream machine learning methods of point cloud segmentation cannot provide high efficiency and accuracy when extending to transmission line scenes. This paper proposes a filter-assisted airborne point cloud semantic segmentation for transmission lines. First, a large number of ground point clouds is identified by introducing the well-developed cloth simulation filter to alleviate the impact of the imbalance of the target object proportion on the classifier’s performance. The multi-dimensional features are then defined, and the classification model is trained to achieve the multi-element semantic segmentation of the transmission line scene. The experimental results and analysis indicate that the proposed filter-assisted algorithm can significantly improve the semantic segmentation performance of the transmission line point cloud, enhancing both the point cloud segmentation efficiency and accuracy by more than 25.46% and 3.15%, respectively. The filter-assisted point cloud semantic segmentation method reduces the volume of sample data, the number of sample classes, and the sample imbalance index in power line scenarios to a certain extent, thereby improving the classification accuracy of classifiers and reducing time consumption. This research holds significant theoretical reference value and engineering application potential for scene reconstruction and intelligent understanding of airborne laser point cloud transmission lines.

Research on wireless channel model based on improved ray tracing algorithm that considers multiple diffuse scattering and employs pyramid-shaped ray tubes as the carrier

This paper extensively utilizes fine three dimensional environmental data obtained from laser point clouds. Based on theories such as geometrical optics and effective roughness theory, a deterministic wireless channel model is established, which integrates higher-order diffuse scattering. This model is referred to as the ray tracing fusion with higher-order diffuse scattering model. To expedite the collision calculation between rays and the scene, this paper introduces a combined approach of voxelization and signed distance field, resulting in a remarkable 16-fold improvement in computational speed. Moreover, aiming to balance accuracy and efficiency, the paper systematically analyzes the optimization computation parameters of the model. Finally, the proposed model is validated using measurement data in the frequency range of 1 GHz to 6 GHz in mountainous terrain. The results indicate that the predicted outcomes of the proposed model have an accuracy within 6 dB compared to the measurement results, and are superior to ITU-R P.1546, which is an international standard recommended by the International Telecommunication Union for modeling electromagnetic wave propagation in undulating terrain. This provides necessary technical support for network planning and optimization.

Two-Staged Technology for CoCr Stent Production by SLM

Additive manufacturing of porous materials with a specific macrostructure and tunable mechanical properties is a state-of-the-art area of material science. Additive technologies are widely used in industry due to numerous advantages, including automation, reproducibility, and freedom of design. Selective laser melting (SLM) is one of the advanced techniques among 3D fabrication methods. It is widely used to produce various medical implants and devices including stents. It should be noticed that there is a lack of information on its application in stent production. The paper presents the technological aspects of CoCr stent SLM fabrication, including design of stents and development of regimes for their manufacturing. Physical, chemical, and technological properties of CoCr powder were initially determined. Parametric design of mesh stent models was adopted. A two-stage approach was developed to ensure dimensional accuracy and quality of stents. The first stage involves a development of the single-track fusion process. The second stage includes the stent manufacturing according to determined technological regimes. The single-track fusion process was simulated to assign laser synthesis parameters for stent fabrication. Melting bath temperature and laser regimes providing such conditions were determined. Twenty-seven SLM manufacturing regimes were realized. Dependence of single-tracks width and height on the laser power, exposition time, and point distance was revealed. The qualitative characteristics of tracks imitating the geometry of the stent struts as well as favorable and unfavorable fusion regimes were determined. The results of surface roughness regulating of the stents’ structural elements by various methods were analyzed. Thus, this two-staged approach can be considered as a fundamental approach for CoCr stent SLM fabrication.

An automated system for 2D building detection from UAV-based geospatial datasets

The focus of this manuscript is on integrating optical images and laser point clouds carried on low-cost UAVs to create an automated system capable of generating urban city models. After pre-processing both datasets, we co-registered both datasets using the DLT transformation model. We estimated structure heights from the LiDAR dataset through a progressive morphological filter followed by removing bare ground. Unsupervised and supervised image classification techniques were applied to a six-band image created from the optical and LiDAR datasets. After finding building footprints, we traced their edges, outlined their borderlines, and identified their geometric boundaries through several image processing and rule-based feature identification algorithms. Comparison between manually digitized and automatically extracted buildings showed a detection rate of about 92.3 % with an average of 7.4 % falsely identified areas with the six-band image in contrast to classifying only the RGB image that detected about 63.2 % of the building pixels with 25.3 % pixels incorrectly identified. Moreover, our building detection rate with the 6-band image was superior to that attained by performing traditional image segmentation for only the LiDAR DEM. Shifts in the horizontal coordinates between corner points identified by a human operator and those detected by the proposed system were in the range of 10–15 cm. This is an improvement over traditional satellite and manned-aerial large mapping systems that have lower accuracies due to sensor limitations and platform altitude. These findings demonstrate the benefits of fusing multiple UAV remote sensing datasets over utilizing a single dataset for urban area mapping and 3D city modeling.

High-precision 3D Modeling of Construction Waste Pile Bodies by Integrating Multi-source Data

Abstract. The instability of construction waste pile bodies, as an increasingly concerning disaster, poses significant risks to the safety of people's lives and property. Currently, there is limited research on high-precision 3D modeling techniques for construction waste pile bodies, which significantly hinders the accuracy and reliability of early detection and risk assessment of pile body instability. Therefore, constructing high-precision 3D models of construction waste pile bodies using multi-source data plays a crucial role in improving the accuracy and timeliness of early warning systems for pile body instability, offering significant theoretical research and practical application value. Building 3D models of construction waste pile bodies solely based on unmanned aerial vehicle (UAV) oblique photography data faces challenges, such as various degrees of data voids and insufficient model completeness, and few methods currently address the construction of 3D models of construction waste pile bodies through the fusion of multi-source data. This paper attempts to use the Iterative Closest Point (ICP) algorithm, combining SLAM laser point cloud data with UAV oblique photography data to fill data voids, and utilizes the fused point cloud to reconstruct the triangulation network, achieving the transformation from 3D point cloud models to 3D surface models. On the basis of texture mapping, a high-precision 3D model of the construction waste pile body is constructed. The research results show that the 3D model of the construction waste pile body, integrated with multi-source data, has a planar error of 0.0187m and an elevation error of 0.0368m, meeting the corresponding model accuracy requirements. It can more realistically restore the fine 3D features of the construction waste pile body, effectively compensating for the shortcomings of single-source data in 3D modeling of construction waste pile bodies, providing a new method for the 3D model reconstruction of construction waste pile bodies, and offering effective data support for construction waste research.

LUOJIA Explorer: An Auto-UAV for Unexposed Space Exploration

Abstract. With the advancement of technology, unexposed spaces have emerged as a new type of strategic area, attracting significant attention from researchers. These spaces often present complex environments, such as extreme lighting variations, irregular geometries, and the lack of external positioning signals, making human entry hazardous. Fortunately, advancements in robotics and artificial intelligence have enabled unmanned systems to mitigate these challenges, allowing for safer exploration of unexposed spaces. UAVs, as vital unmanned system platforms, are extensively used across various industrial scenarios and are particularly effective for detecting unexposed spaces. To facilitate automated exploration of these areas, we propose the autonomous UAV system, LUOJIA Explorer. Equipped with a LiDAR sensor, data transmission module, and external anti-collision devices to protect the aircraft, LUOJIA Explorer is designed for effective unexposed space detection. We have integrated a laser point cloud positioning and mapping subsystem, along with a planning and control subsystem, into the LUOJIA Explorer. Through both simulation and actual experiments, the Luojia Explorer has demonstrated the capability to achieve stable flight based on self-positioning in unexposed spaces, with an exploration efficiency of 88.83 m3/s.

A Practice of City-scale 3D Geographic Entity Representation and Application in China: The Smart Chongqing City Centre

Abstract. The Chinese photogrammetry industry is currently experiencing a significant revolution driven by digital intelligence technologies. Traditional city-scale 3D modelling, typically reconstructed from oblique photogrammetry or laser points, is facing increasing limitations in refined expression, real-time rendering, machine recognition, data sharing, and interoperability due to its large data volume and mesh structure. These limitations make it difficult to meet the substantial demands of spatial analysis, simulation, and prediction. The concept of geographic entities, which are machine-recognizable spatial data structures, is anticipated to provide a competitive solution for these challenges. This work employs 3D scene modelling and geographic knowledge graphs to construct geographic entities with semantic relationships at city-scale, addressing the limitations encountered in the application of traditional mesh/point-based 3D models. A case study in Chongqing city centre demonstrates the feasibility and effectiveness of the proposed approach and its potential applications in simulation, prediction, early warning, and feedback for urban planning and smart city management.

Effects of Constraining Postural Sway During Upper-Limb Precision Aiming Task Practice in Individuals with Stroke

Individuals post-stroke commonly demonstrate alterations in motor behavior with regard to both task performance and the motor strategies used in pursuit of task goals. We evaluated whether constraining postural sway (motor strategy) during practice would affect upper-limb precision aiming performance (task performance) and postural control adaptations. Adults with stroke stood on a force plate while immersed in a virtual scene displaying an anterior target. Participants aimed to position a virtual laser pointer (via handheld device) in the target. Participants then completed practice trials involving aiming at a lateral target. For this practice session, participants were randomized to either (a) a “constraint” group wherein they received physical constraint to limit postural sway, or (b) a “no-constraint” group. Task performance and postural control were assessed before and after practice, and transfer to another upper-limb task was evaluated. After practice, both groups improved paretic upper-limb performance. For the target task, the no-constraint group showed task-sensitive changes in postural control. The constraint group showed no changes in postural control. At transfer, the constraint group increased postural sway. Constraining postural sway after stroke should be carefully considered with the recognition that postural sway arises from exploratory movements involved in the discovery of adaptable motor solutions.

Powder bed fusion of solid and permeable Crofer 22 APU parts for applications in chemical process engineering

AbstractAdditive manufacturing of metals has attracted significant attention across various industries. However, its adoption in certain chemical and energy sectors remains constrained by the absence of optimized alloys tailored for high temperatures and corrosive environments. This study explores the laser-based powder bed fusion processing of Crofer 22 APU, a high-temperature, corrosion-resistant alloy ideal for applications such as methane steam reforming reactors, high-temperature fuel cells, and palladium membrane supports. Systematic optimization of laser parameters including laser power, hatch distance, point distance, and building angles was conducted for both dense and porous objects. Dense parts achieved a relative density of 0.9999 and surface roughness of Sa= 41.27±1.48 on a 45$$^\circ$$ ∘ overhang. Porous samples showed a porosity range of 28.655.5% and surface roughness Sa 22.2 $$\upmu$$ μ m to 45.9 $$\upmu$$ μ m as hatch distance increased from 0.12 mm to 0.16 mm. Additionally, an 8YSZ coating applied via screen printing demonstrated the impact of hatch distance and laser power on surface quality. The combination of additive manufacturing of Crofer 22 APU and screen printing of 8YSZ simplifies the preparation of metallic supports for Pd-based membranes, reducing fabrication time and cost by shortening the number of preparation steps. This technique offers a promising approach for scaling up membranes and membrane reactors.

Laser L ight I nduced R etinopathy in a Young Boy: A Case Report from Eye Care Hospital, Karachi

A case report of a 14 year-old-boy who came with a history of direct gazing to laser pointer at Khairun'nisa Eye Hospital, Karachi. A comprehensive eye examination including Slit lamp biomicroscopy, Fundoscopy, optical coherence tomography and Fundus photography was done. Main outcome measure were right eye foveal scarring with normal periphery. Fundoscopy showed right Eye old scar at fovea, exactly like solar retinopathy. Solar retinopathy is a type of retinopathy which is caused by directly looking at sunlight. In this report we are presenting the classical case of retinopathy exactly like solar retinopathy due to gazing directly at laser light for a long period of time.

ICESat-2 single photon laser point cloud denoising algorithm based on improved DBSCAN clustering

AbstractThe Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) has great potential for development due to its advantages of the use of multiple beams, low energy consumption, high repetition frequency, and high measurement sensitivity. However, the weak photon signal emitted by the photon counting lidar is susceptible to the background noise caused by the sun and the atmosphere, which can seriously affect the processing and application of laser data. This paper proposes an improved DBSCAN clustering algorithm for denoising single photon laser point clouds in mountainous areas. Firstly, a grouping method based on elevation and distance statistics is proposed to reduce the influence of terrain undulations on denoising accuracy. Finally, an automatic radius search method is put forward to determine clustering radius of each group, automatically find the optimal radius, and improve the existing DBSCAN clustering method. The method proposed in this paper is compared with the classical DBSCAN algorithm. The results show that the proposed algorithm significantly improves denoising accuracy in mountainous areas and effectively filters out most background noise. Graphical Abstract

Procedure Design and Reliability Analysis for Prediction of Surface Subsidence of a Metal Mine Induced by Block Caving Method—A Case Study of Pulang Copper Mine in China

Surface subsidence resulting from block caving mining causes considerable environmental and economic harm in mining areas, highlighting the critical need for accurate predictions of surface subsidence. Given the unique features of the block caving technique and the resemblance between the released ore pillars and the mining processes, this paper developed a lightweight model to forecast surface settlement utilizing the probability integration approach to address the issue of predicting surface settlement in metallic mines. This study focuses on the Pulang Copper Mine, situated in the northeast of Shangri-La County within the Yunnan Province, as a case example. This mine employs the block caving method, which results in substantial surface subsidence. A visual mining simulation program is designed to combine the ore mining plan with the prediction model, manage the ore output of each mining point in batches, treat the ore pillars released in the planning cycle as strip work, and simulate and calculate the surface area above the ore pillars settlement value. The calculated values of surface subsidence induced by ore drawing are then interpreted as the downward displacement of the surface subsidence beneath the strip workings. Furthermore, to verify the reliability of the model, three-dimensional laser point cloud data of the Pulang Copper Mine in recent years were collected, and the differences between the predicted surface and the measured surface were calculated and analyzed.

Does Embodiment in Virtual Reality Boost Learning Transfer? Testing an Immersion-Interactivity Framework

This study investigates the role of embodiment when learning a technical procedure in immersive virtual reality (VR) by introducing a framework based on immersion and interactivity. The goal is to determine how increasing the levels of immersion and interactivity affect learning experiences and outcomes. In a 2 × 2 factorial design, 177 high school students were assigned to one of four experimental conditions, varying levels of immersion (learning in immersive virtual reality wearing a head-mounted display (VR) vs. learning via a computer screen (PC)) and interactivity (directly manipulating objects using controllers/mouse and keyboard (congruent) vs. indirectly manipulating objects with a laser pointer to select a course of action (incidental)). The main outcome measure was a transfer task in which students were required to perform the task they had learned in the virtual environment using concrete objects in real life. Results demonstrated that students in the VR conditions experienced significantly higher levels of presence, agency, location, body ownership, and embodied learning compared to participants in the PC conditions. Additionally, students’ performance during the virtual lesson predicted their real-life transfer test. However, there were no significant effects of immersion or interactivity on any of the transfer measures. The results suggest that high immersion in VR can increase self-reported measures of presence, agency, location, body ownership, and embodied learning among students. However, increased embodiment—manipulated by adding immersion and congruent manipulation of objects did not improve transfer.

A period of backwards walking affects the craniovertebral angle, proprioception, balance, and foot pressure on forward head posture of female students

PurposeThe presence of spinal arches is crucial for maintaining good body posture, reducing pressure on joints, and preventing deformities caused by misalignment. Engaging in exercises that focus on correcting body alignment, such as walking backward, can enhance posture and various related factors. MethodA group of 46 female students suffering from forward head posture was selected (age: 22.89 ± 2.31 years, height: 1.63 ± 0.05 m, weight: 60.01 ± 5.41 kg, body mass index: 22.38 ± 1.28 kg/m2) and randomly divided into two groups. The intervention group participated in three exercise sessions per week for four weeks, while the control group did not receive any intervention during the study. Changes in the craniovertebral angle, proprioception, resistance, and foot pressure were measured using photogrammetry, head-neck angle examination with a laser pointer, a balance measuring device, and a foot scanner. FindingsThe results of this study showed that the training program had a significant effect on the craniovertebral angle (P = 0.001), neck proprioception (P = 0.001), balance (P = 0.001), and sole pressure (P = 0.001). Likewise, the results of the group comparison revealed significant differences in various variables, such as the craniovertebral angle (P = 0.001), neck proprioception (P = 0.001), balance (P = 0.001), and plantar pressure (P ≥ 0.05). ConclusionThis study demonstrated that walking backward enhanced the craniovertebral angle, proprioception, balance, and alterations in the plantar center of pressure. Therefore, this exercise is recommended as a complementary therapeutic approach for individuals with forward head posture.

Extraction of Features of Regular Surfaces from the Laser Point Clouds for 3D Objects

Extraction of Features of Regular Surfaces from the Laser Point Clouds for 3D Objects

Research on differentiated lightning protection application of power lines based on laser point cloud data

Abstract The collection and processing of data in lightning protection assessment of transmission lines takes a long time and accuracy is difficult to ensure. Therefore, a laser point cloud data registration method based on pole and tower material coordinate transformation is proposed for differential lightning protection evaluation of transmission lines. Using DBSCAN algorithm, combined with point cloud data voxel filtering and noise reduction methods, to achieve data segmentation of transmission line tower body. Based on the characteristics of the symmetrical structure of the tower body, the coordinate transformation method is adopted to achieve the registration of multiple point cloud data. The parameters in the tower structure are obtained by concatenating and fusing elements such as the wire point cloud, lightning protection line point cloud, and tower point cloud. After verification by lightning protection assessment, the proposed method simplifies the processing flow of data compliance detection during the assessment process, reduces the time cost of lightning protection assessment, and greatly improves the accuracy and effectiveness of lightning risk assessment results.

Research on 3D laser SLAM algorithm based on graph optimization

Abstract Aiming at the problems that the positioning accuracy of laser Simultaneous Localization and Mapping (SLAM) is affected by the misclassification of close feature points and incorrect feature matching in the environment with redundant features, a 3D laser SLAM algorithm based on graph optimization was proposed. Firstly, to reduce the feature extraction error problem, the distance threshold is used to filter the close point cloud and improve the accuracy of feature classification, enhanced reliability of optoelectronic measurement data. Secondly, to improve the consistency of feature matching, based on the rough matching based on k-dimensional tree (KD-Tree), a two-stage fine matching based on geometric consistency is introduced to improved laser point alignment accuracy. The proposed method is compared with the mainstream localization algorithms based on KITTI dataset. Compared with Lidar-IMU Odometry with Sparse Adaptive Mapping and Fast LiDAR-Inertial Odometry, the positioning error of the proposed method is reduced by 21.88%and 24.43%on average, which showsthe superiority of the present algorithm in the application of optoelectronics and optical technology, and provides new ideas and methods for the development of high-precision laser navigation technology.

The histological comparison of the effect of 50mW green and blue laser on the human eye at variable time intervals: an animal model.

To compare the effects of 50mW green and blue laser pointers on retinal tissue with different exposure durations. The experimental, histopathological study was conducted from November 2021 to April 2022 at the Postgraduate Laboratory of the Department of Physiology and Medical Physics, College of Medicine, Mustansiriyah University, Iraq, and comprised adult male mice. They were divided into green group G and blue group B. Group G was exposed to a 50mW green laser pointer with wavelength 532nm, while group B was exposed to a blue laser pointer with wavelength 415nm. The groups were subdivided with respect to duration of exposure. Subgroups G1 and B1 had exposure time 1min, G2 and B2 had exposure time 2min and G3 and B3 had exposure time 3min. Across the groups, the overall supplied power density was 130mW/cm2. Conventional Haemotoxylin and Eosin stain was used to assess the damage on the retinal tissues of the mice post-exposure. Data was analysed using SPSS 24. Of the 24 groups, 12(50%) were in each of the 2 groups, while each subgroup had 4(16.66%) mice. There was more retinal tissue damage in group G compared to group B (p<0.05). The damage increased significantly with increase in exposure duration in both groups (p<0.05). The traditional green laser pointers caused more damage to the retina of mice than the blue laser.