Search Results (122)

Laser ranging technology holds a key position in the military, aerospace, and industrial fields due to its high precision and non-contact measurement characteristics. As a core component, the performance of the photon detector directly determines the ranging accuracy and range. This paper systematically reviews the technological development of photonic detectors for laser ranging, with a focus on analyzing the working principles and performance differences of traditional photodiodes [PN (P-N junction photodiode), PIN (P-intrinsic-N photodiode), and APD (avalanche photodiode)] (such as the high-frequency response characteristics of PIN and the internal gain mechanism of APD), as well as their applications in short- and medium-range scenarios. Additionally, this paper discusses the unique advantages of special structures such as transmitting junction-type and Schottky-type detectors in applications like ultraviolet light detection. This article focuses on photon counting technology, reviewing the technological evolution of photomultiplier tubes (PMTs), single-photon avalanche diodes (SPADs), and superconducting nanowire single-photon detectors (SNSPDs). PMT achieves single-photon detection based on the external photoelectric effect but is limited by volume and anti-interference capability. SPAD achieves sub-decimeter accuracy in 100 km lidars through Geiger mode avalanche doubling, but it faces challenges in dark counting and temperature control. SNSPD, relying on the characteristics of superconducting materials, achieves a detection efficiency of 95% and a dark count rate of less than 1 cps in the 1550 nm band. It has been successfully applied in cutting-edge fields such as 3000 km satellite ranging (with an accuracy of 8 mm) and has broken through the near-infrared bottleneck. This study compares the differences among various detectors in core indicators such as ranging error and spectral response, and looks forward to the future technical paths aimed at improving the resolution of photon numbers and expanding the full-spectrum detection capabilities. It points out that the new generation of detectors represented by SNSPD, through material and process innovations, is promoting laser ranging to leap towards longer distances, higher precision, and wider spectral bands. It has significant application potential in fields such as space debris monitoring. Full article

In this paper, we experimentally investigated the excitation of spoof surface plasmon polaritons (SSPPs) supported by a 1D subwavelength grating with a rectangular profile in the terahertz (THz) frequency range. Using the attenuated total reflection technique and the THz radiation of the Novosibirsk free electron laser, we carried out detailed studies of both angular and gap spectra at several wavelengths. A shallow grating supporting a fundamental mode was fabricated by means of multibeam X-ray lithography and used as a test sample. The results indicated that we achieved 1-THz tunability of resonance in the frequency range from 1.51 to 2.54 THz on a single grating, which cannot be obtained with active tunable metamaterials. The Q factors of the resonances in the angular spectra were within the range of 19.4–37.6, while the resonances of the gap spectra had a Q factor lying within the 1.17–2.03 range. The gap adjustment capability of the setup shown in the work has great potential in modulation of the absorption efficiency, whereas the angular tuning and recording data from each point of the grating will enable real-time monitoring of changes in the surrounding medium. All of this is highly important for enhanced terahertz real-time absorption spectroscopy and imaging. Full article

Point clouds from laser scanners have been widely used in recent research on indoor modeling methods. Currently, particularly in data-driven modeling methods, data preprocessing for dividing structural components and nonstructural components is required before modeling. In this paper, we propose an indoor modeling method without the classification of structural and nonstructural components. A pre-mesh is generated for constructing the adjacency relations of point clouds, and plane components are extracted using planar-based region growing. Then, the distance fields of each plane are calculated, and voxel data referred to as a surface confidence map are obtained. Subsequently, the inside and outside of the indoor model are classified using a graph-cut algorithm. Finally, indoor models with watertight meshes are generated via dual contouring and mesh refinement. The experimental results showed that the point-to-mesh error ranged from approximately 2 mm to 50 mm depending on the dataset. Furthermore, completeness—measured as the proportion of original point-cloud data successfully reconstructed into the mesh—approached 1.0 for single-room datasets and reached around 0.95 for certain multiroom and synthetic datasets. These results demonstrate the effectiveness of the proposed method in automatically removing non-structural components and generating clean structural meshes. Full article

To address the problem of excessive ablation in conventional laser processing caused by the inhomogeneous energy distribution at the focal point, along with the inherent heterogeneity and surface irregularities of textile materials, a new method for laser printing elastic bandage fabrics was developed. We used flat top light sources, short focal field mirrors, and low power lasers instead of the Gaussian light sources, long focal field mirrors, and high-power lasers used in traditional methods. First, the sample was preheated, and the aspherical lens system was designed and simulated. Then, the physical and chemical properties of laser-processed elastic bandage fabrics were investigated. Finally, based on single-factor experiments, orthogonal experimental analysis was conducted to determine the optimal process parameters. The results show that the optimized optical path can effectively improve the uniformity of the temperature field during laser scanning and enhance focusing performance; as energy gradually accumulates, chemical bonds in polymer molecules break; when the elastic bandage fabric is in a highly elastic state, it exhibits appropriate breaking strength and color difference. The best parameters obtained from the single-factor experiment are as follows: laser power range of 25–34 W, scanning speed range of 2200–2800 mm/s, preheating temperature range of 125–200 °C. The best parameters obtained from the orthogonal experiment are as follows: laser power 28 W, scanning speed 2800 mm/s, and the preheating temperature 175 °C. Full article

We propose a pulsed modulated digital holographic microscopy (PM-DHM) technique for the dynamic measurement of flowing microparticles in microfluidic systems. By digitally tuning the pulse width and the repetition rate of a laser source within a single-frame exposure, this method enables the recording of multiple images of flowing microparticles at different time points within a single hologram, allowing the quantification of velocity and acceleration. We demonstrate the feasibility of PM-DHM by measuring the velocity, acceleration, and forces exerted on PMMA microspheres and red blood cells flowing in microfluidic chips. Compared to traditional frame-sampling-based imaging methods, this technique has a much higher time resolution (in a range of microseconds) that is limited only by the pulse duration. This method demonstrates significant potential for high-throughput label-free flow cytometry detection and offers promising applications in drug development and cell analysis. Full article

Common single-line 2D LiDAR sensors and cameras have become core components in the field of robotic perception due to their low cost, compact size, and practicality. However, during the data fusion process, the randomness and complexity of real industrial scenes pose challenges. Traditional calibration methods for LiDAR and cameras often rely on precise targets and can accumulate errors, leading to significant limitations. Additionally, the semantic fusion of LiDAR and camera data typically requires extensive projection calculations, complex clustering algorithms, or sophisticated data fusion techniques, resulting in low real-time performance when handling large volumes of data points in dynamic environments. To address these issues, this paper proposes a semantic fusion algorithm for LiDAR and camera data based on contour and inverse projection. The method has two remarkable features: (1) Combined with the ellipse extraction algorithm of the arc support line segment, a LiDAR and camera calibration algorithm based on various regular shapes of an environmental target is proposed, which improves the adaptability of the calibration algorithm to the environment. (2) This paper proposes a semantic segmentation algorithm based on the inverse projection of target contours. It is specifically designed to be versatile and applicable to both linear and arc features, significantly broadening the range of features that can be utilized in various tasks. This flexibility is a key advantage, as it allows the method to adapt to a wider variety of real-world scenarios where both types of features are commonly encountered. Compared with existing LiDAR point cloud semantic segmentation methods, this algorithm eliminates the need for complex clustering algorithms, data fusion techniques, and extensive laser point reprojection calculations. When handling a large number of laser points, the proposed method requires only one or two inverse projections of the contour to filter the range of laser points that intersect with specific targets. This approach enhances both the accuracy of point cloud searches and the speed of semantic processing. Finally, the validity of the semantic fusion algorithm is proven by field experiments. Full article

Deformation monitoring of deep foundation pits is critical for ensuring construction safety. However, traditional methods (e.g., inclinometers) face inherent challenges such as limited spatial coverage (<30% in large-scale projects), low operational efficiency (requiring 2–3 times longer data acquisition than 3D scanning), and spatiotemporal discontinuity (single-point measurements fail to capture 3D dynamic deformation fields, leading to incomplete mechanical interpretations of soil–structure interactions). In contrast, 3D laser scanning provides rapid, non-contact, and high-resolution data acquisition that can capture comprehensive deformation fields over large areas. Therefore, this study proposes a novel deformation monitoring framework, aiming to expand the monitoring range and enhance the measurement accuracy. The proposed framework combines the extensive spatial coverage of 3D laser scanning with the corrective capability of a backpropagation neural network (BPNN) model. The proposed approach leverages sparse yet high-precision traditional monitoring data to train the BPNN, effectively correcting systematic deviations in laser scanning measurements caused by external disturbances and instrument errors. Validation at an active deep foundation pit site in Hangzhou reveals that the method reduces the mean absolute error (MAE) from 5.2 mm to 1.8 mm, with corrected scanning data consistency exceeding 80 percent compared to conventional monitoring measurements. This work establishes a scalable framework for deformation analysis and sets a technical benchmark for monitoring in large-scale deep foundation pit projects. Full article

The boiling point of metals is dependent on the ambient pressure. Therefore, in laser-based fusion welding and additive manufacturing processes, the resulting process regime, ranging from heat conduction welding to the keyhole mode, is also influenced by the process pressure. While laser welding deliberately uses reduced process pressures to achieve the keyhole mode with a lower laser power input as well as a more stable keyhole, there are no positive findings on the laser powder bed fusion process (PBF-LB/M) under vacuum conditions so far. Furthermore, the literature suggests that the process window is significantly reduced, particularly in the high vacuum regime. However, this work demonstrates that components made of the ferritic steel 22NiMoCr3-7 can be successfully manufactured at low process pressures of ${2 \times 10}^{-2} \mathrm{mbar}$ using a double-scanning strategy. The strategy consists of a first scan with a defocused laser beam, where the powder is preheated and partially sintered, followed by a second scan with a slightly defocused laser beam, in which the material within a single layer is completely melted. To test this manufacturing strategy, 16 test cubes were manufactured to determine the achievable relative densities and tensile specimens were produced to assess the mechanical properties. Metallographic analysis of the test cubes revealed that relative densities of up to 98.48 ± 1.43% were achieved in the test series with 16 different process parameters. The tensile strength determined ranged from 722 to 724 MPa. Additionally, a benchmark part with complex geometric features was successfully manufactured in a high vacuum atmosphere without the need for a complex parameterization of individual part zones in the scanning strategy. Full article

Human tracking is a fundamental technology for mobile robots that work with humans. Various devices are used to observe humans, such as cameras, RGB-D sensors, millimeter-wave radars, and laser range finders (LRF). Typical LRF measurements observe only the surroundings on a particular horizontal plane. Human recognition using an LRF has a low computational load and is suitable for mobile robots. However, it is vulnerable to variations in human height, potentially leading to detection failures for individuals taller or shorter than the standard height. This work aims to develop a method that is robust to height differences among humans using a 3D LiDAR. We observed the environment using a 3D LiDAR and projected the point cloud onto a single horizontal plane to apply a human-tracking method for 2D LRFs. We investigated the optimal height range of the point clouds for projection and found that using 30% of the point clouds from the top of the measured person provided the most stable tracking. The results of the path-following experiments revealed that the proposed method reduced the proportion of outlier points compared to projecting all the points (from 3.63% to 1.75%). As a result, the proposed method was effective in achieving robust human following. Full article

Since 1969, 55 years ago, Lunar Laser Ranging (LLR) has provided accurate and precise (down to ~1 cm RMS) measurements of the Moon’s orbit thanks to the Apollo and Lunokhod Cube Corner Retroreflector (CCR) Laser Retroreflector Arrays (LRAs) deployed on the Moon. Nowadays, the current level of precision of these measurements is largely limited by the lunar librations affecting the old generation of LRAs. To improve this situation, next-generation libration-free retroreflectors are necessary. To this end, the Satellite/lunar/GNSS laser ranging/altimetry and cube/microsat Characterization Facilities Laboratory (SCF_Lab) at the Istituto Nazionale di Fisica Nucleare—Laboratori Nazionali di Frascati (INFN-LNF), in collaboration with the University of Maryland (UMD) and supported by the Italian Space Agency (ASI), developed MoonLIGHT (Moon Laser Instrumentation for General relativity High-accuracy Tests), a single large CCR with a front face diameter of 100 mm, nominally unaffected by librations, and with optical performances comparable to the Apollo/Lunokhod LRAs of CCRs. Such a big CCR (hereafter, ML100) is mounted into a specifically devised, designed, and manufactured robotic actuator, funded by the European Space Agency (ESA), the so-called MoonLIGHT Pointing Actuator (MPAc), which, once its host craft has landed on the Moon, will finely align the front face of the ML100 towards the Earth. The (optical) performances of such a piece of hardware, MoonLIGHT+MPAc, were tested in/by the SCF_Lab in order to ensure that it was space flight ready before its integration onto the deck of the host craft. After its successful deployment on the Moon, additional and better-quality LLR data (down to ~ 1 mm RMS or better for the contribution of the laser retroreflector instrument, MoonLIGHT, to the total LLR error budget) will be available to the community for future and enhanced tests of gravitational theories. Full article

Geometric calibration, as a crucial method for ensuring the precision of spaceborne single-photon laser point cloud data, has garnered significant attention. Nonetheless, prevailing geometric calibration methods are generally limited by inadequate precision or are unable to accommodate spaceborne lasers equipped with multiple payloads on a single platform. To overcome these limitations, a novel geometric calibration method for spaceborne single-photon lasers that integrates laser detectors with corner cube retroreflectors (CCRs) is introduced in this study. The core concept of this method involves the use of triggered detectors to identify the laser footprint centerline (LFC). The geometric relationships between the triggered CCRs and the LFC are subsequently analyzed, and CCR data are incorporated to determine the coordinates of the nearest laser footprint centroids. These laser footprint centroids are then utilized as ground control points to perform the geometric calibration of the spaceborne single-photon laser. Finally, ATLAS observational data are used to simulate the geometric calibration process with detectors and CCRs, followed by conducting geometric calibration experiments with the gt2l and gt2r beams. The results demonstrate that the accuracy of the calibrated laser pointing angle is approximately 1 arcsec, and the ranging precision is better than 2.1 cm, which verifies the superiority and reliability of the proposed method. Furthermore, deployment strategies for detectors and CCRs are explored to provide feasible implementation plans for practical calibration. Notably, as this method only requires the positioning of laser footprint centroids using ground equipment for calibration, it provides exceptional calibration accuracy and is applicable to single-photon lasers across various satellite platforms. Full article

In response to the limitations of existing vehicle detection technologies when applied to distributed sensor networks for road traffic holographic perception, this paper proposes a vehicle information detection technology based on single-point laser ranging. The system uses two single-point laser radars with fixed angles, combined with an adaptive threshold state machine and waveform segmentation fusion, to accurately detect vehicle speed, lane position, and other parameters. Compared with traditional methods, this technology offers advantages such as richer detection dimensions, low cost, and ease of installation and maintenance, making it suitable for large-scale traffic monitoring on secondary roads, highways, and suburban roads. Experimental results show that the system achieves high accuracy and reliability in low-to-medium-traffic flow scenarios, demonstrating its potential for intelligent road traffic applications. Full article

Background/Objectives: Oral candidiasis is an opportunistic infection caused by Candida species. Recently, antifungal drugs have become less effective due to yeast resistance, emphasizing the need for new treatment strategies. This study aimed to assess the effect of the Er:YAG laser on the inhibition of growth and elimination of mature single-species Candida biofilms. Methods: The study utilized reference strains of C. albicans, C. glabrata, C. parapsilosis, and C. krusei organized in single-species biofilms on Sabouraud dextrose agar (SDA). First part: Candida suspensions (0.5 McFarland standard) were spread on SDA plates—two for each strain. Er:YAG laser irradiation was applied in a single pulse mode, 30 to 400 mJ, to 32 predetermined points. The growth inhibition zones (GIZs) were measured at 24–96 h of incubation. Second part: biofilms were prepared similarly and, after 96 h of incubation, exposed to Er:YAG laser irradiation at different energies (50, 100, 150, 200 mJ) for 180 s, per 1.44 cm area. Post-irradiation, impressions were taken using Rodac Agar to determine yeast counts. The count of colony-forming units (CFU) after irradiation was measured and results were analysed statistically. Results: First part: GIZ was found in all irradiated sites, with various Candida strains. The results showed a significant increase in the width of GIZ in the energy range of 30–280 mJ and a non-significant increase in the energy range of 300–400 mJ. Second part: the number of CFU remaining after the irradiation of biofilms with 150 mJ energy differed statistically significantly from other results obtained after using 50, 100, or 200 mJ energy, regardless of the Candida strain tested. Conclusions: The Er:YAG is shown to have good disinfecting properties (inhibiting biofilm growth, even at low-energy doses (50 mJ), and eliminating maturity, Candida spp. biofilms most effective on the 150 mJ energy dose). Full article

Beach surface moisture (BSM) is crucial to studying coastal aeolian sand transport processes. However, traditional measurement techniques fail to accurately monitor moisture distribution with high spatiotemporal resolution. Remote sensing technologies have garnered widespread attention for providing rapid and non-contact moisture measurements, but a single method has inherent limitations. Passive remote sensing is challenged by complex beach illumination and sediment grain size variability. Active remote sensing represented by LiDAR (light detection and ranging) exhibits high sensitivity to moisture, but requires cumbersome intensity correction and may leave data holes in high-moisture areas. Using machine learning, this research proposes a BSM inversion method that fuses UAV (unmanned aerial vehicle) orthophoto brightness with intensity recorded by TLSs (terrestrial laser scanners). First, a back propagation (BP) network rapidly corrects original intensity with in situ scanning data. Second, beach sand grain size is estimated based on the characteristics of the grain size distribution. Then, by applying nearest point matching, intensity and brightness data are fused at the point cloud level. Finally, a new BP network coupled with the fusion data and grain size information enables automatic brightness correction and BSM inversion. A field experiment at Baicheng Beach in Xiamen, China, confirms that this multi-source data fusion strategy effectively integrates key features from diverse sources, enhancing the BP network predictive performance. This method demonstrates robust predictive accuracy in complex beach environments, with an RMSE of 2.63% across 40 samples, efficiently producing high-resolution BSM maps that offer values in studying aeolian sand transport mechanisms. Full article

In this paper, we face the point-cloud segmentation problem for spinning laser sensors from a deep-learning (DL) perspective. Since the sensors natively provide their measurements in a 2D grid, we directly use state-of-the-art models designed for visual information for the segmentation task and then exploit the range information to ensure 3D accuracy. This allows us to effectively address the main challenges of applying DL techniques to point clouds, i.e., lack of structure and increased dimensionality. To the best of our knowledge, this is the first work that faces the 3D segmentation problem from a 2D perspective without explicitly re-projecting 3D point clouds. Moreover, our approach exploits multiple channels available in modern sensors, i.e., range, reflectivity, and ambient illumination. We also introduce a novel data-mining pipeline that enables the annotation of 3D scans without human intervention. Together with this paper, we present a new public dataset with all the data collected for training and evaluating our approach, where point clouds preserve their native sensor structure and where every single measurement contains range, reflectivity, and ambient information, together with its associated 3D point. As experimental results show, our approach achieves state-of-the-art results both in terms of performance and inference time. Additionally, we provide a novel ablation test that analyses the individual and combined contributions of the different channels provided by modern laser sensors. Full article