Geometric Features Research Articles

Enhancing antibacterial efficacy through macrocyclic host complexation of fluoroquinolone antibiotics for overcoming resistance

The use of supramolecular assemblies in pharmaceuticals has garnered significant interest. Recent studies have shown that the activities of antibacterial agents can be enhanced through complexation with cyclic oligomers and metal ions. Notably, these complexes sometimes possess greater therapeutic properties than the parent drugs. To develop microbiologically potent supramolecular drugs, the complexation of macrocyclic hosts with fluoroquinolone (FQ) antibiotics was investigated. FQs are a successful family of antibiotics that target the bacterial enzymes DNA gyrase and DNA topoisomerase IV, leading to bacterial cell death through the inhibition of DNA synthesis. However, antibiotic resistance resulting from the repeated use of FQs over time has limited their effectiveness against resistant pathogens. To overcome this issue, the encapsulation of FQs in polyphenolic macrocycles was investigated. This study highlights resorcinarene, a polyphenolic host with antibacterial properties, and its ability to chemically interact with FQs. The inclusion complexation process was analyzed using NMR and FTIR techniques. The binding constants determined by 1H-NMR titration revealed that levofloxacin forms more stable complexes with resorcinarene than with β-cyclodextrin, which aligned with MD simulations. Assessment of the geometric characteristics of the inclusion complexes using 2D NMR analysis confirmed that different moieties of various FQs can fit into a single host cavity and improve activity against gram-negative bacteria. Overall, these findings suggest that encapsulation in polyphenolic macrocycles is a promising strategy for utilizing FQs against antibiotic-resistant bacteria.

Size-Resolved Shape Evolution in Inorganic Nanocrystals Captured via High-Throughput Deep Learning-Driven Statistical Characterization.

Precise size and shape control in nanocrystal synthesis is essential for utilizing nanocrystals in various industrial applications, such as catalysis, sensing, and energy conversion. However, traditional ensemble measurements often overlook the subtle size and shape distributions of individual nanocrystals, hindering the establishment of robust structure-property relationships. In this study, we uncover intricate shape evolutions and growth mechanisms in Co3O4 nanocrystal synthesis at a subnanometer scale, enabled by deep-learning-assisted statistical characterization. By first controlling synthetic parameters such as cobalt precursor concentration and water amount then using high resolution electron microscopy imaging to identify the geometric features of individual nanocrystals, this study provides insights into the interplay between synthesis conditions and the size-dependent shape evolution in colloidal nanocrystals. Utilizing population-wide imaging data encompassing over 441,067 nanocrystals, we analyze their characteristics and elucidate previously unobserved size-resolved shape evolution. This high-throughput statistical analysis is essential for representing the entire population accurately and enables the study of the size dependency of growth regimes in shaping nanocrystals. Our findings provide experimental quantification of the growth regime transition based on the size of the crystals, specifically (i) for faceting and (ii) from thermodynamic to kinetic, as evidenced by transitions from convex to concave polyhedral crystals. Additionally, we introduce the concept of an "onset radius," which describes the critical size thresholds at which these transitions occur. This discovery has implications beyond achieving nanocrystals with desired morphology; it enables finely tuned correlation between geometry and material properties, advancing the field of colloidal nanocrystal synthesis and its applications.

Exploring the use of atmospheric freeze drying for dehydrating pharmaceutics in vials: Baseline water sublimation investigation

Atmospheric freeze-drying (AFD) is a relatively new freeze-drying technology without the need for a vacuum, making it easy to operate and cost-effective. Although there have been studies using AFD to dehydrate solid foods, there is currently no research on using AFD to dehydrate frozen liquids in pharmaceutical vials. In this study, several approaches were evaluated to enhance the atmospheric sublimation of water in pharmaceutical vials. Using −4 °C impinging jet airflow to dry frozen water samples in the vial, it was found that convective action significantly affected the sublimation rate. On this basis, a 3D-printed air-guide model was designed to improve airflow circulation in the vial, and it was found that the drying rate was highest when airflow energy loss was minimized, and airflow velocity at the sample surface was maximized. Additionally, the geometric characteristics of the vial also influenced the sublimation rate; vials with a larger bottom area and shorter height showed the highest sublimation rate. Increasing the vial’s bottom radius from 11 mm to 13 mm, under atmospheric pressure and using cold air at approximately −5 °C, reduced the drying time of 1 g of frozen water from 8.5 h to 6 h; each 5 mm height increase added 0.5 h to the drying time. Using cold air at −10 °C to dry 1 g of frozen water in a 5 mL vial, the combination of ultrasonic-induced energy (at a frequency of 39.46 kHz) and the air-guide model effectively reduced the sublimation time from 7 h to 5 h, compared to using only the air-guide model. However, this technique may be vulnerable to melting at the vial-transducer contact point, should the transducer be directly attached to the vials.

Geometric analysis and local buckling behavior of wire arc additively manufactured ER110S cruciform section stub columns

The recent prominence and potential of wire arc additive manufacturing (WAAM) in the civil engineering industry have gained significant attention due to its ability to produce complex geometric shapes in a cost-effective and flexible manner. This paper presents the geometric analysis and local buckling behavior of WAAM ER110S cruciform section stub columns. A total of eight specimens with a broad range of width-to-thickness ratios were manufactured. 3D laser-scanning was conducted to capture the geometric characteristics of all the WAAM ER110S cruciform section stub column specimens. Material testing with coupons cut from various orientations of WAAM ER110S plates was performed and this is followed by the stub column testing where digital image correlation (DIC) was employed to monitor the full-field deformations of the specimens. The obtained test data were then employed to assess the applicability of current design rules specified in European, American and Australian standards to WAAM ER110S cruciform section stub columns. The assessment results revealed that (i) the current Class 3 slenderness limits (i.e. slender/non-slender slenderness limits) can be safely used for the classification of outstand plate elements of cruciform sections in compression and (ii) all the three specifications can lead to safe though marginally scattered failure load predictions of WAAM ER110S cruciform section stub columns.

Инженерный расчет гибких оболочек лесосплавных опор

Currently, the issue of delivering wood raw materials from remote forest areas, where the largest volumes are located, is relevant. Often, this delivery is economically justified only when using a network of medium and small rivers with the use of environmentally friendly technologies. On such rivers, it is preferable to use mobile anchors, in particular shore fillable ones, for fixing timber-rafting objects. A description has been given of a new modification of such an anchor, which differs from the prototype by a container having a flexible shell. This allows to significantly reduce the material consumption for manufacturing the container, the dimensions of the anchor in the transport position, and to produce anchors of any realistic size. The choice of the most rational option for such a container implies the scientific justification of its parameters. The aim of the study is to develop a scientific basis for this justification. Analytical formulas have been theoretically obtained for determining the basic geometric characteristics of the cross-section of the container under consideration. These formulas are useful in scientific research, but their application is very problematic in practical engineering calculations. This is due to the dependence of the specified characteristics on elliptic integrals and parameters, which are very difficult to measure in engineering practice. Having performed calculations using these formulas for a single cross-sectional area, we have obtained its specific geometric characteristics for various shapes, i.e. for various width-to-height ratios of a container to be filled. The results of the calculations have been approximating dependencies linking the named characteristics with the specified ratio, which is called the shape factor. Multiplying the specific characteristics by the square root of the cross-sectional area gives the absolute values of the corresponding characteristics. As a result, we have obtained formulas convenient for practical use, allowing to determine the width and height of the container to be filled, the width of its base, the perimeter of the cross-section, the heights of the zero pressure line and the extreme lateral point of the cross-section above the base of the container. The accuracy of calculations based on these and analytical formulas is almost the same. A formula for determining the specific tension of a flexible container shell has been obtained. The nature and degree of influence of the shape factor and length of the container on its other geometric characteristics have been established. An algorithm for substantiating the key parameters of a container to be filled with a flexible shell has been developed using the described results.

A Geometric Significance-Aware Deep Mutual Learning Network for Building Extraction from Aerial Images

Knowledge-driven building extraction method exhibits a restricted adaptability scope and is vulnerable to external factors that affect its extraction accuracy. On the other hand, data-driven building extraction method lacks interpretability, heavily relies on extensive training data, and may result in extraction outcomes with building boundary blur issues. The integration of pre-existing knowledge with data-driven learning is essential for the intelligent identification and extraction of buildings from high-resolution aerial images. To overcome the limitations of current deep learning building extraction networks in effectively leveraging prior knowledge of aerial images, a geometric significance-aware deep mutual learning network (GSDMLNet) is proposed. Firstly, the GeoSay algorithm is utilized to derive building geometric significance feature maps as prior knowledge and integrate them into the deep learning network to enhance the targeted extraction of building features. Secondly, a bi-directional guidance attention module (BGAM) is developed to facilitate deep mutual learning between the building feature map and the building geometric significance feature map within the dual-branch network. Furthermore, the deployment of an enhanced flow alignment module (FAM++) is utilized to produce high-resolution, robust semantic feature maps with strong interpretability. Ultimately, a multi-objective loss function is crafted to refine the network’s performance. Experimental results demonstrate that the GSDMLNet excels in building extraction tasks within densely populated and diverse urban areas, reducing misidentification of shadow-obscured regions and color-similar terrains lacking building structural features. This approach effectively ensures the precise acquisition of urban building information in aerial images.

Investigation on the influence of mesoscale geometric features on the surface white layer characteristics in titanium alloy milling using ball end mills

A white layer will be generated on the machined surface when milling titanium alloy. Its existence will make the surface of the workpiece more brittle, and it is easy to produce cracks during the machining process, which greatly harms machining. Aiming at this problem, this paper takes the ball-end milling cutter with mesoscopic geometric characteristics as the research object, establishes the theoretical model of the white layer thickness of the workpiece under the micro-texture and cutting edge factors, analyzes the single factor of micro-texture parameters and cutting edge parameters, and the interaction between them and cutting parameters on the milling force-thermal characteristics and the white layer of the workpiece. The influence law reveals its mechanism from the macro and micro perspectives. It uses the white layer thickness as the evaluation index to optimize the mesoscopic geometric characteristic parameters by using the simulated annealing algorithm. The results demonstrate that the mesoscopic geometric feature of the ball-end milling cutter effectively reduces the thickness of the white layer, achieving a 38% reduction compared to that observed with a non-textured milling cutter. The thickness of the white layer exhibits a positive correlation with the distance L from the blade and a negative correlation with the edge radius R. The interaction between the cutting edge radius R and the cutting depth a p influences the milling force and heat, alters the micro-texture of the workpiece surface, induces the α + β phase transformation, and affects the white layer thickness. Based on the predictive model for white layer thickness, the optimization results for comprehensive milling performance are as follows: R is 59.39 μm, L is 110.01 μm, D is 58.68 μm, L1 is 130.59 μm, a p is 0.30 mm, V is 147.39 mm/min, f is 0.08 mm/z.

3D-BIOPRINTING OF BONE TISSUE IN VITRO И IN SITU

Advanced techniques in bone tissue regeneration include innovative achievements in the field of tissue engineering, among which 3D bioprinting technology holds a special place. This method demonstrates significant progress, driven by its numerous advantages, confirmed through extensive scientific research. 3D bioprinting offers the capability of precise printing of bone transplants with specified geometry, incorporating necessary biomaterials such as cells and growth factors, which facilitates effective osteoinduction, osteoconduction, and vascularization. This review article analyzes various modern methods of bone tissue restoration, including the use of autotransplants, allotransplants, and synthetic bone transplants. Particular attention is paid to the latest innovations in 3D bioprinting, opening prospects for creating individualized bone tissues characterized by a high degree of biocompatibility and minimizing the need for invasive procedures. Additionally, the article presents the concept of in situ bioprinting, developed by Weiss and his colleagues in 2007, as a method of direct printing of bio-inks directly in the area of damage in a living organism. This progressive approach provides high precision in the restoration of tissue defects, regardless of their complexity and geometric features. Over the last decade, several successful clinical cases of using printed structures have been registered, demonstrating the significance of 3D bioprinting in treating life-threatening diseases, as well as in accelerating the regeneration process and creating tissue transplants for subsequent implantation.

Combination of LiDAR detection and green integral method for calculating irregular cross-section geometric properties of deteriorated components in timber historic buildings

Timber components in historic buildings are susceptible to deterioration due to the complex external environment. The loss of material in these components and changes in their cross-section geometry significantly impact the stability and load-bearing capacity of historic buildings. A portable LiDAR device is utilized to acquire the cross-section geometry of the deteriorated components. The irregular cross-section profiles are extracted using point cloud slicing and the Alpha-Shape algorithm. Geometric characteristics such as cross-section area, moment of inertia, and moment of resistance are calculated using Green's integral method. The performance of this method is evaluated by measuring eight groups of deteriorated components with different cross-section characteristics and comparing it with five traditional methods. The results demonstrate that the proposed method improves the calculation accuracy by 1%–21% relative to traditional methods, thereby enhancing reliability by 9%–54%. Additionally, it reduces manual calculation workload and human error, while promoting calculation efficiency. The method exhibits excellent applicability and high efficiency, with an error below 3% when compared to the true value. This method proves valuable in evaluating the extent of deterioration in historic timber components and provides essential data for preventive conservation and restoration of historic timber buildings.

Overall plan optimization of BWB UAV based on comprehensive evaluation

Based on the shape and performance characteristics of the wing-body fusion layout UAV, a multidisciplinary comprehensive analysis model, comprehensive evaluation model and optimization model suitable for the overall scheme of the UAV in the conceptual design stage are established, and the corresponding tools are developed. The geometric, weight, aerodynamic and stealth characteristics of the overall scheme of the wing-body fusion layout UAV are analyzed by combining numerical analysis and engineering methods. The improved TOPSIS method is used to establish a comprehensive evaluation model from the perspective of the loading capacity, economy and adaptability of the UAV. The rationality of the analysis model is verified by taking multiple UAV schemes as examples. Using the parallel subset simulation optimization algorithm, the traditional multi-objective optimization is completed with the maximum cruise lift-to-drag ratio and the minimum forward RCS as the goal, and the scheme optimization based on comprehensive evaluation is completed with the strongest competitiveness as the goal. Based on all the schemes generated by the multi-objective optimization process, the parameter correlation analysis is completed, and the influence of the span, sweep angle and twist angle on the aerodynamic and stealth characteristics is verified. The RCS of the final multi-objective optimization scheme is reduced 41.6%, and the cruise lift-to-drag ratio is improved 3.5% ; the competitiveness score of the comprehensive evaluation optimization scheme is improved 44.0%.

A Drone-Based Structure from Motion Survey, Topographic Data, and Terrestrial Laser Scanning Acquisitions for the Floodgate Gaps Deformation Monitoring of the Modulo Sperimentale Elettromeccanico System (Venice, Italy)

The structural deformation monitoring of civil infrastructures can be performed using different geomatic techniques: topographic measurements with total stations and levels, TLS (terrestrial laser scanning) acquisitions, and drone-based SfM (structure from motion) photogrammetric surveys, among others, can be applied. In this work, these techniques are used for the floodgate gaps and the rubber joints deformation monitoring of the MOSE system (Modulo Sperimentale Elettromeccanico), the civil infrastructure that protects Venice and its lagoon (Italy) from high waters. Since the floodgates are submerged most of the time and cannot be directly measured and monitored using high-precision data, topographic surveys were performed in accessible underwater tunnels. In this way, after the calculation of the coordinates of some reference points, the coordinates of the floodgate corners were estimated knowing the geometric characteristics of the system. A specific activity required the acquisition of the TLS scans of the stairwells in the shoulder structures of the Treporti barrier because many of the reference points fixed on the structures were lost during the placement of elements on the seabed. They were replaced with new points whose coordinates in the project/as-built reference system were calculated by applying the Procrustean algorithm by means of homologous points. The procedure allowed the estimation of the transformation parameters with maximum residuals of less than 2.5 cm, a value in agreement with the approximation of the real concrete structures built. Using the obtained parameters, the coordinates of the new reference points were calculated in the project reference system. Once the 3D orientation of all caissons in the barrier was reconstructed, the widths of the floodgate gaps were estimated and compared with the designed values and over time. The obtained values were validated in the Treporti barrier using a drone-based SfM photogrammetric survey of the eight raised floodgates, starting from the east shoulder caisson. The comparison between floodgate gaps estimated from topographic and TLS surveys, and those obtained from measurements on the 3D photogrammetric model, provided a maximum difference of 1.6 cm.

Closed Circuit of 3-Dimensional Polymer Powders

This article describes the most important stages of testing the levels of innovative readiness of a new machine and material construction solutions for closed loops of polymer powders in 3D additive manufacturing. The aim of this study was to indicate the state and directions of development of the technology of geometric recycling of polymer powders in 3D additive manufacturing, the need to control the geometric quality of the powders, and the quality of the design of machines for their processing in a closed circuit. The method was aimed at creating a strategy and verifying the stages of development of a new idea for stabilizing the geometric and dynamic design features of polymer powders for additive manufacturing (3D printing). Connections and relationships of the variable design features of powders, machines and devices with variable postulated states of products and processes allowed for the analysis, knowledge, description and development of variables for stabilizing geometric features of polymer powders in recycling. The results show the possibilities of supporting innovative creativity according to the adopted method, allow for determining the level of compliance of the quality of the products of the technical system, the effectiveness of preparatory processes for recycling and the non-toxicity of the products and processes of the new technology and their closed loop. The goal of developing useful design values, according to an integrated method of analysis, assessment and environmental development, was achieved, including the proposal of design features of razor blade shredders, supported by genetic algorithms, and devices for stabilizing polymer powders in additive manufacturing with recycling.

Research on detection and classification of automotive body surface defects based on image processing and machine learning

Abstract The high noise in the automotive body surface image makes it difficult to extract defects. Moreover, a single feature cannot describe the complex automotive body surface defects leading to low classification accuracy. This paper proposes a highly robust method for classifying body surface defects. Firstly, an edge detection method that integrates the wavelet transform and the mathematical morphology is applied to detect defects. Subsequently, the geometric features of detected defects are combined with scale-invariant feature transform features to be the classification basis. Finally, the classification accomplishes through a support vector machine(SVM) with the parameters optimized via the grey wolf optimizer-SVM. Experimental results show the proposed classification method based on feature fusion achieves an average of 93% accuracy in automotive body surface defects classification and exhibits a 100% classification accuracy for pseudo-defects, which demonstrates the fusion of the wavelet transform and the mathematical morphology for automotive body surface defects detection can effectively reduce the impact of image noise for ensuring the extracted edges are intact.

A new path planning strategy driven by geometric features and tensile properties for 3D printing of continuous fiber reinforced thermoplastic composites

Three-dimensional (3D) printing technology for continuous fiber reinforced thermoplastic composites (C-FRTP), capable of rapid manufacturing of lightweight components with intricate geometric features, has emerged as one of the most promising technologies in the field of advanced composite manufacturing. Path planning is a crucial step for determining the fabrication quality of C-FRTP components. This study proposed a new 3D printing path planning strategy driven by the geometric features and tensile properties of C-FRTP components. The strategy employed the properties of the Euler graph to generate the continuous full-field filling paths, ensuring the geometric features of the target components. The intersections were scattered along the printing path to enhance the tensile strength. The feasibility and advantages of the new path planning strategy were validated by comparative experiments with different printing paths. The results indicated that the new strategy not only achieved the geometric features of the target components but significantly enhanced their tensile strength. Using the printing path generated by the new path planning strategy, the tensile strength of specimens featuring mounting holes reached 349.4 MPa, which was only about 4.1 % lower than the tensile strength of continuous fibers at straight paths. Compared to the existing contour-parallel path, the new strategy in this work improved the tensile properties by about 40.9 %. The new path planning strategy proposed in this study shows great potential to design and fabricate C-FRTP components with enhanced mechanical properties for practical applications.

Engineering Fano resonances in plasmonic metasurfaces for colorimetric sensing and structural colors

In this paper, we present the design and fabrication of a plasmonic metasurface based on titanium dioxide (TiO2) nanowire arrays integrated with plasmonic layers. The structure is engineered to produce Fano resonances within the visible spectrum, resulting from the coupling of localized surface plasmon resonances, lattice modes, and nanowire's optical modes. Experimentally, we show that by tuning the geometrical features of the metasurface, such as the length, diameter, and period of the nanowires, a high-quality factor single peak can be achieved in the reflection spectra, resulting in vivid structural colors in bright field. To our knowledge, this is the first demonstration of such vivid colors with nanowire arrays in bright field reflections. When characterized by refractive index fluids around the refractive index of water, the plasmonic metasurface also showed great potential for biochemical colorimetric sensing. The best design demonstrated a bulk sensitivity of 183 nm/RIU with high Q resonance features and linear changes in color values using image processing.

Three-dimensional numerical analysis of the effect of foundation stiffness on soil-structure interaction

Stiffness-related geometric characteristics are closely linked to the performance of structural systems. The influence of foundation stiffness on the soil-structure interaction (SSI) mechanism was investigated by numerical modeling of different building foundations. Numerical tools (PLAXIS 3D and TQS/CAD) were used for decoupling analyses of the interaction between the structural components. Results obtained from instruments installed in a building case study were used to refine the numerical calculations. Linear elastic constitutive models and beam elements were useful in reducing computational processes and ensuring study feasibility. The simulation results provided a satisfactory representation of field observations. Interaction between piles and the environment governs the relationship between foundation stiffness and uniform settlement. Rigid pile groups are achieved with greater pile length, diameter and spacing. This study highlights the different effects of pile geometric parameters on loading and unloading rates in pillars, demonstrating that the SSI mechanism is more sensitive to less rigid foundations, particularly those with greater pile embedment. It was concluded that numerical simulations to predict settlement as realistically as possible require understanding and selecting the most appropriate model and type of calculation.

Geometric morphological variation characteristics of loropetalum chinense leaves (r. Br.) Oliv. (hamamelidiaceae) and their response to intra-specific competition

This study aimed to explore the geometric morphological variation characteristics of the leaves of the understory plant Loropetalum chinense under different competitive pressures. Specifically, the leaf morphological indices were calculated, and multiple comparison analysis and Fisher linear discriminant model were employed to decipher the morphological variability based on contour outlines. The results revealed that the leaf morphological indices exhibited significant differences among various sample plots. The lower stand density and DBH (diameter at breast height) facilitated more water and air exchange and led to a rounded leaf shape. The results provided a theoretical basis for the study of response of leaf morphological evolution to forest competition. Bangladesh J. Bot. 53(3): 681-687, 2024 (September) Special

Robust and correspondence-free point cloud registration: an extended approach with multiple hypotheses evaluation

AbstractPoint cloud registration is a fundamental problem in robotics, critical for tasks such as localization and mapping. Most approaches to this problem use feature-based techniques. However, these approaches have issues when dealing with unstructured environments where meaningful features are difficult to extract. Recently, an innovative global point cloud registration algorithm, PHASER, which does not rely on geometric features or point correspondences, has been introduced. It leverages Fourier transforms to identify the optimal rigid transform that maximizes cross-correlation between source and target point clouds. PHASER can also incorporate additional data channels, like LiDAR intensity, to enhance registration results. Because it does not rely on local features and because of its ability to exploit additional data, PHASER is particularly useful when dealing with very noisy point clouds or with many outliers. For this reason, we propose an extension to PHASER that considers multiple plausible rototranslation hypotheses. Our extended approach outperforms the original PHASER algorithm, especially in challenging scenarios where point clouds are widely separated. We validate its effectiveness on the DARPA SubT, and the Newer College datasets, showcasing its potential for improving registration accuracy in complex environments.

Dynamic indoor mapping for AVP: Crowdsourcing mapping without prior maps

AbstractHigh‐definition maps are essential for autonomous vehicle navigation, but indoor parking lots remain poorly mapped due to high costs. To address this, a crowdsourcing model gathers data from consumer‐grade sensors in mass‐produced vehicles to create semantic maps. Indoor parking lots lack GNSS signals, and most of them do not have high‐definition maps or navigation maps as references, making it difficult to ensure the accuracy of the final mapping results. Additionally, the semantic information of indoor parking lots is relatively limited, and the geometric features are overly similar, which significantly impacts the accuracy of point cloud registration. Therefore, this article proposes a crowdsourcing‐based approach, where vehicles generate local semantic maps at the client end and upload them to the cloud. Leveraging the scene characteristics of indoor parking lots, the cloud optimizes and fits a large amount of crowdsourced data to obtain a high‐precision base map without prior information. Enhanced ICP point cloud registration merges subsequent maps with the base. Additionally, parking space occupancy information is provided. This map can furnish the necessary information for Autonomous Valet Parking (AVP) tasks. Evaluation on the BEVIS dataset shows a root mean square error of 0.482446 m for vehicle localization on the cloud‐based map.

A precise registration method for large-scale urban point clouds based on phased and spatial geometric features

Abstract The dense high-rise buildings and multipath effects in urban areas significantly reduce the positioning signal accuracy of laser scanning systems, leading to layering and offset issues in the collected point cloud data on the same road. In order to acquire comprehensive and consistent three-dimensional information on the objects, thereby providing field inspection data for large-scale road traffic network scenarios, in this paper, an improved point cloud registration method is proposed to divide the registration process into two stages: elevation registration and plane registration. Elevation registration takes the ground point cloud as the registration primitive, reduces the number of point clouds through curvature down-sampling, and constrains the feature point sequence with a fixed range to provide a good initial pose for fine registration. The plane registration first inherits the elevation registration parameters, combining the dynamic distance parameters of spherical region step based on the median, using robust multi-scale loss functions to address residual points, effective adjacent point pairs are selected to obtain the spatial transformation matrix, and realizes accurate registration. Experimental results with multiple sets of urban point cloud data show that the root mean square error of point cloud registration can be controlled within 0.06 m, achieving a relatively superior registration accuracy, it can provide detailed prior data for measurement information analysis.