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Polymer-derived ceramics (PDCs) are increasingly recognized as promising materials for high-temperature thin/thick film sensors (HTTFSs). However, a large linewidth and poor dimensional accuracy have severely restricted the practical application of PDCs HTTFSs. Our study employed a picosecond laser to precisely pattern the polymer-derived ceramic (PDC) thick film while applying no obvious damage to the interface between ceramic and substrate. Through parameters optimization, the laser-affected area was minimized to 6 μm wide, and a minimum feature size of about 30 μm was achieved. Furthermore, 7 rounds of high-temperature tests of the patterned film revealed good temperature-resistance repeatability within a temperature range of up to 800 °C. To demonstrate practical application potential, we successfully fabricated a heat flux sensor, and it exhibited a sensitivity of 1.32 mV/(kW/m2), a response time of 600 ms, and excellent repeatability over a range of 0–75.4 kW/m2. In summary, this paper proposes a novel approach toward miniaturizing the linewidth of the polymer-derived ceramics thick film sensor, showing significant potential for precise measurements in extreme environments. [Display omitted] [ABSTRACT FROM AUTHOR]

Multimodal perception represents a vital approach for observing non-cooperative targets in space. The accurate determination of the extrinsic parameters for monocular camera and LiDAR systems serves as the cornerstone for achieving effective space multimodal perception. However, due to factors like launch and operation, these extrinsic parameters can experience drift, thereby compromising the overall performance of the multimodal perception system. To tackle this challenge, this study introduces a novel approach: an edge feature-based external calibration method for monocular camera and LiDAR using in-orbit data. The proposed methodology involves several steps. Firstly, edges are extracted from the image, followed by distance transformation based on the edge image. The subsequent integration of the edge image and distance image yields the search scene image. Then, edge feature is extracted from the point cloud data. Finally, after constructing the objective function, genetic algorithms are used to determine precise external parameters. Simulation results underscore the effectiveness of the proposed algorithm, affirming its ability to achieve high-precision calibration results for external parameters even within intricate spatial environments. [ABSTRACT FROM AUTHOR]

This study details an advanced shovel point optimization system for unmanned loaders, crucial for efficient shovelling operations. First, the shovel point evaluation index is established with reference to the driver's experience. Second, a novel method for pile profile reconstruction is proposed, utilizing a trained neural network to detect piles and extracting the point cloud using LiDAR and camera fusion. Subsequently, the system employs optimization algorithm to identify the best shovel point. Finally, 62 consecutive working experiments are successfully conducted. The system's performance closely approximates the driver's choices and achieves an average bucket fill factor of 97.7% for four materials. Results demonstrate the proposed method is reliable and efficient and contributes to the development of automated construction machinery. [ABSTRACT FROM AUTHOR]

3D object detection is a challenging and promising task for autonomous driving and robotics, benefiting significantly from multi-sensor fusion, such as LiDAR and cameras. Conventional methods for sensor fusion rely on a projection matrix to align the features from LiDAR and cameras. However, these methods often suffer from inadequate flexibility and robustness, leading to lower alignment accuracy under complex environmental conditions. Addressing these challenges, in this paper, we propose a novel Bidirectional Attention Fusion module, named BAFusion, which effectively fuses the information from LiDAR and cameras using cross-attention. Unlike the conventional methods, our BAFusion module can adaptively learn the cross-modal attention weights, making the approach more flexible and robust. Moreover, drawing inspiration from advanced attention optimization techniques in 2D vision, we developed the Cross Focused Linear Attention Fusion Layer (CFLAF Layer) and integrated it into our BAFusion pipeline. This layer optimizes the computational complexity of attention mechanisms and facilitates advanced interactions between image and point cloud data, showcasing a novel approach to addressing the challenges of cross-modal attention calculations. We evaluated our method on the KITTI dataset using various baseline networks, such as PointPillars, SECOND, and Part-A2, and demonstrated consistent improvements in 3D object detection performance over these baselines, especially for smaller objects like cyclists and pedestrians. Our approach achieves competitive results on the KITTI benchmark. [ABSTRACT FROM AUTHOR]

Extrinsic parameter calibration is the foundation and prerequisite for LiDAR and camera data fusion of the autonomous system. This technology is widely used in fields such as autonomous driving, mobile robots, intelligent surveillance, and visual measurement. The learning-based method is one of the targetless calibrating methods in LiDAR and camera calibration. Due to its advantages of fast speed, high accuracy, and robustness under complex conditions, it has gradually been applied in practice from a simple theoretical model in just a few years, becoming an indispensable and important method. This paper systematically summarizes the research and development of this type of method in recent years. According to the principle of calibration parameter estimation, learning-based calibration algorithms are divided into two categories: accurate calibrating estimation and relative calibrating prediction. The evolution routes and algorithm frameworks of these two types of algorithms are elaborated, and the methods used in the algorithms' steps are summarized. The algorithm mechanism, advantages, limitations, and applicable scenarios are discussed. Finally, we make a summary, pointing out existing research issues and trends for future development. [ABSTRACT FROM AUTHOR]

The turfgrass industry supports golf courses, sports fields, and the landscaping and lawn care industries worldwide. Identifying the problem spots in turfgrass is crucial for targeted remediation for turfgrass treatment. There have been attempts to create vehicle- or drone-based scanners to predict turfgrass quality; however, these methods often have issues associated with high costs and/or a lack of accuracy due to using colour rather than grass height (R2 = 0.30 to 0.90). The new vehicle-mounted turfgrass scanner system developed in this study allows for faster data collection and a more accurate representation of turfgrass quality compared to currently available methods while being affordable and reliable. The Gryphon Turf Canopy Scanner (GTCS), a low-cost one-dimensional LiDAR array, was used to scan turfgrass and provide information about grass height, density, and homogeneity. Tests were carried out over three months in 2021, with ground-truthing taken during the same period. When utilizing non-linear regression, the system could predict the percent bare of a field (R2 = 0.47, root mean square error < 0.5 mm) with an increase in accuracy of 8% compared to the random forest metric. The potential environmental impact of this technology is vast, as a more targeted approach to remediation would reduce water, fertilizer, and herbicide usage. [ABSTRACT FROM AUTHOR]

Given the importance of constraining cloud droplet number concentrations (Nd) in low-level clouds, we explore two methods for retrieving Nd from surface-based remote sensing that emphasize the information content in lidar measurements. Because Nd is the zeroth moment of the droplet size distribution (DSD), and all remote sensing approaches respond to DSD moments that are at least 2 orders of magnitude greater than the zeroth moment, deriving Nd from remote sensing measurements has significant uncertainty. At minimum, such algorithms require the extrapolation of information from two other measurements that respond to different moments of the DSD. Lidar, for instance, is sensitive to the second moment (cross-sectional area) of the DSD, while other measures from microwave sensors respond to higher-order moments. We develop methods using a simple lidar forward model that demonstrates that the depth to the maximum in lidar-attenuated backscatter (Rmax⁡) is strongly sensitive to Nd when some measure of the liquid water content vertical profile is given or assumed. Knowledge of Rmax⁡ to within 5 m can constrain Nd to within several tens of percent. However, operational lidar networks provide vertical resolutions of > 15 m, making a direct calculation of Nd from Rmax⁡ very uncertain. Therefore, we develop a Bayesian optimal estimation algorithm that brings additional information to the inversion such as lidar-derived extinction and radar reflectivity near the cloud top. This statistical approach provides reasonable characterizations of Nd and effective radius (re) to within approximately a factor of 2 and 30 %, respectively. By comparing surface-derived cloud properties with MODIS satellite and aircraft data collected during the MARCUS and CAPRICORN II campaigns, we demonstrate the utility of the methodology. [ABSTRACT FROM AUTHOR]

To date, only a limited number of studies have utilized remote sensing imagery to estimate aboveground biomass (AGB) in the Miombo ecoregion using wall-to-wall medium resolution optical satellite imagery (Sentinel-2 and Landsat), localized airborne light detection and ranging (lidar), or localized unmanned aerial systems (UAS) images. On the one hand, the optical satellite imagery is suitable for wall-to-wall coverage, but the AGB estimates based on such imagery lack precision for local or stand-level sustainable forest management and international reporting mechanisms. On the other hand, the AGB estimates based on airborne lidar and UAS imagery have the precision required for sustainable forest management at a local level and international reporting requirements but lack capacity for wall-to-wall coverage. Therefore, the main aim of this study was to investigate the use of UAS-lidar as a sampling tool for satellite-based AGB estimation in the Miombo woodlands of Zambia. In order to bridge the spatial data gap, this study employed a two-phase sampling approach, utilizing Sentinel-2 imagery, partial-coverage UAS-lidar data, and field plot data to estimate AGB in the 8094-hectare Miengwe Forest, Miombo Woodlands, Zambia, where UAS-lidar estimated AGB was used as reference data for estimating AGB using Sentinel-2 image metrics. The findings showed that utilizing UAS-lidar as reference data for predicting AGB using Sentinel-2 image metrics yielded superior results (Adj-R2 = 0.70, RMSE = 27.97) than using direct field estimated AGB and Sentinel-2 image metrics (R2 = 0.55, RMSE = 38.10). The quality of AGB estimates obtained from this approach, coupled with the ongoing advancement and cost-cutting of UAS-lidar technology as well as the continuous availability of wall-to-wall optical imagery such as Sentinel-2, provides much-needed direction for future forest structural attribute estimation for efficient management of the Miombo woodlands. [ABSTRACT FROM AUTHOR]

Road safety is a serious concern worldwide, and traffic signs play a critical role in confirming road safety, particularly in the context of AVs. Therefore, there is a need for ongoing advancements in traffic sign evaluation methodologies. This paper comprehensively analyzes the relationship between traffic sign retroreflectivity and LiDAR intensity to enhance visibility and communication on road networks. Using Python 3.10 programming and statistical techniques, we thoroughly analyzed handheld retroreflectivity coefficients alongside LiDAR intensity data from two LiDAR configurations: 2LRLiDAR and 1CLiDAR systems. The study focused specifically on RA1 and RA2 traffic sign classes, exploring correlations between retroreflectivity and intensity and identifying factors that may impact their performance. Our findings reveal variations in retroreflectivity compliance rates among different sign categories and color compositions, emphasizing the necessity for targeted interventions in sign design and production processes. Additionally, we observed distinct patterns in LiDAR intensity distributions, indicating the potential of LiDAR technology for assessing sign visibility. However, the limited correlations between retroreflectivity and LiDAR intensity underscore the need for further investigation and standardization efforts. This study provides valuable insights into optimizing traffic sign effectiveness, ultimately contributing to improved road safety conditions. [ABSTRACT FROM AUTHOR]

Background: The use of Computed Tomography (CT) imaging data to create 3D printable patient‐specific devices for radiation oncology purposes is already well established in the literature and has shown to have superior conformity than conventional methods. Using non‐ionizing radiation imaging techniques such as photogrammetry or laser scanners in‐lieu of a CT scanner presents many desirable benefits including reduced imaging dose and fabrication of the device can be completed prior to simulation. With recent advancements in smartphone‐based technology, photographic and LiDAR‐based technologies are more readily available than ever before and to a high level of quality. As a result, these non‐ionizing radiation imaging methods are now able to generate patient‐specific devices that can be acceptable for clinical use. Purpose: In this work, we aim to determine if smartphones can be used by radiation oncologists or other radiation oncology staff to generate bolus or brachytherapy surface moulds instead of conventional CT with equivalent or comparable accuracy. Methods: This work involved two separate studies: a phantom and participant study. For the phantom study, a RANDO anthropomorphic phantom (limited to the nose region) was used to generate 3D models based on three different imaging techniques: conventional CT, photogrammetry & LiDAR which were both acquired on a smartphone. Virtual boli were designed in Blender and 3D printed from PLA plastic material. The conformity of each printed boli was assessed by measuring the air gap volume and approximate thickness between the phantom & bolus acquired together on a CT. For the participant study, photographs, and a LiDAR scan of four volunteers were captured using an iPhone 13 Pro™ to assess their feasibility for generating human models. Each virtual 3D model was visually assessed to identify any issues in their reconstruction. The LiDAR models were registered to the photogrammetry models where a distance to agreement analysis was performed to assess their level of similarity. Additionally, a 3D virtual bolus was designed and printed using ABS material from all models to assess their conformity onto the participants skin surface using a verbal feedback method. Results: The photogrammetry derived bolus showed comparable conformity to the CT derived bolus while the LiDAR derived bolus showed poorer conformity as shown by their respective air gap volume and thickness measurements. The reconstruction quality of both the photogrammetry and LiDAR models of the volunteers was inadequate in regions of facial hair and occlusion, which may lead to clinically unacceptable patient‐specific device that are created from these areas. All participants found the photogrammetry 3D printed bolus to conform to their nose region with minimal room to move while three of the four participants found the LiDAR was acceptable and could be positioned comfortably over their entire nose. Conclusions: Smartphone‐based photogrammetry and LiDAR software show great potential for future use in generating 3D reference models for radiation oncology purposes. Further investigations into whether they can be used to fabricate clinically acceptable patient‐specific devices on a larger and more diverse cohort of participants and anatomical locations is required for a thorough validation of their clinical usefulness. [ABSTRACT FROM AUTHOR]

To ensure the safety of tunnel construction, it is necessary to accurately and timely measure the deformation of the shotcrete liner during the management of the construction process. The light detection and ranging (LiDAR) technique, known as the reality reproduction technology, provides a new solution for deformation measurement. However, the nonsmooth defects of shotcrete liner and the scattered noise points caused by the nearby dust, as well as a large number of nontunnel structures such as pipes, construction machinery, and vehicles in the obtained tunnel point cloud datasets, hinder the application of point clouds in deformation measurement of shotcrete liner. In this paper, a ring simulation sampling (RSS) method for automatic sampling and deformation measurement of shotcrete liner using the multiepoch LiDAR point clouds is proposed. This method constructs the virtual ring according to shape of the tunnel cross section and emits the rays from the ring to the center of each tunnel cross section. The length difference of the rays arriving at the outer wall of the multiepoch tunnel point clouds is calculated to achieve the analysis of overall and local deformations of the shotcrete liner. The shotcrete liner point clouds generated by the manual modeling and terrestrial laser scanning are tested to validate the effectiveness of the method in extracting the deformation of shotcrete liner. Compared with the conventional total station monitoring, the mean absolute deviation and root mean square error are 1.39 and 1.93 mm, respectively, which indicates that the RSS method could be effectively applied to the deformation measurement of tunnel shotcrete liner. Deformation measurement of tunnel shotcrete liner is crucial to safety of tunnel construction and implementation of dynamic parameter design. This study recommends the light detection and ranging (LiDAR) technique. The main work of this paper is to determine whether the tunnel shotcrete lining has been deformed as a whole or locally by comparing the obtained multiepoch high-precision 3D point clouds. Furthermore, the authors propose a method for automatic sampling and deformation measurement of shotcrete liner using the multiepoch LiDAR point clouds. Compared with conventional deformation measurement methods (i.e., the total station and fiber optics) that capture deformation information from a limited number of monitoring points, LiDAR technology enables the high-precision, omnidirectional, and noncontact acquisition of spatial data within tunnels. This serves as a complementary and enhanced tool to tunnel safety monitoring techniques and methodologies. In addition, the adoption of the method proposed in this paper facilitates the quality check of shotcrete liner, including thickness assessment and smoothness detection. [ABSTRACT FROM AUTHOR]

Methods and means of remote control of the ecological state of the atmosphere are constantly improving. Lidar sensing allows obtaining up-to-date information about natural and technogenic sources of atmospheric pollution. There is a wide range of problems in ecological control, where the deployment of an inexpensive mobile lidar network is required. For this purpose, it is suggested to use Q-switch and MOPA fiber lasers in lidars. Q-switch fiber lasers have a simpler design and are more practical to use. However, pulses from Q-switch lasers have long full-pulse durations. In the present work, a lidar signal inversion method (LSIM) is proposed for solving this problem. Verification and outdoor experimentation of the LSIM was carried out with the reference signal method (RSM). The advantage of the proposed RSM is the minimum number of controllable parameters necessary for LSIM verification and approbation. As a result, the accuracy of the obtained results increased. Thus, the possibility of application of the Q-switch fiber lasers for lidar sensing is shown both theoretically and experimentally. [ABSTRACT FROM AUTHOR]

Optical remote sensing techniques can indicate the properties of objects by observing different modalities (physical quantities) of the backscattered light at different optical wavelengths. Established examples are reflectance, fluorescence, Raman, or depolarization spectroscopy. LiDAR sensing, on the other hand, allows acquiring the geometry of objects by measuring the propagation delay of optical probing signals. Multimodal multispectral (MM) LiDAR combines these capabilities and extends conventional monochromatic LiDAR in both spectral and modal dimensions within a single instrument, thus enriching point cloud data with non-geometric information. The potentially high dimension of MM LiDAR data, however, poses significant challenges for instrumental design, data acquisition, and data processing. MM LiDAR data are structured as several or all modalities are available in each of the spectral channels. The above challenges can thus be mitigated by feature selection (FS), if the structure of the features is taken into account, i.e., if entire spectral channels or entire modalities are selected or omitted. Herein, we focus on the feature selection method for MM LiDAR and propose a multiclass group feature selection algorithm (MGSVM FS) consisting of a structural sparsity-based embedded feature selection method with an all-in-one support vector machine (SVM). It tackles jointly the challenges arising from the high dimension of the MM data and the need for a multiclass classification task while exploiting the structure of the MM data. In addition, we introduce a complete workflow for evaluating the feature selection and for decision-making. We apply the framework to selecting an optimum spectral and modal configuration for remote material classification using an experimental MM LiDAR system that provides reflectance, distance, and degree of linear polarization in 28 spectral channels of 10 nm width. For the experimental investigation, we use MM LiDAR data obtained in a controlled lab environment from thirty specimens of four material classes relevant for construction. Using all three modalities, we find a configuration with only 3 spectral channels that achieves a classification mean-F1 score of 100% within this small dataset. Similar classification performance can also be achieved with only two modalities when using more spectral channels. MGSVM FS improves the classification mean-F1 score by up to 25% as compared to random selection and outperforms two other commonly used filter and embedded feature selection methods, in this application example. The proposed group feature selection algorithm and decision-making are useful for MM LiDAR, providing a link between instrumental design, data acquisition, and data processing. However, they are also transferable to other application fields related to multiclass classification, regression, and knowledge discovery, with features structured in groups. The collected MM feature dataset, the MGSVM FS algorithm, and the evaluation pipeline are accessible online. 1 1 https://github.com/yuhan-yhyh/Dataset_Code_MGSVM-FS-MM-LiDAR.git. [ABSTRACT FROM AUTHOR]

In order to improve the detection accuracy of surrounding targets by self-driving vehicles, a target object detection algorithm (monocular-bird's eye view fusion, Mono-BEVFusion) with fusion of lidar and monocular image data on bird's eye view features is proposed. To construct camera BEV features, the algorithm builds a simple and efficient depth prediction network to predict the depth of camera features, which is supervised with depth truth values based on an explicit supervision method. When constructing the lidar BEV features, the laser point cloud is voxelized into a columnar grid and transformed into BEV features, and the BEV feature fusion network is designed to fuse the laser point cloud BEV features with the camera BEV features, and the fused features are inputted into the target detection framework to obtain the detection results of the target objects (cars, pedestrians, and cyclists). The Mono-BEVFusion algorithm is evaluated using KITTI dataset and real-vehicle road-harvesting data, and the experimental results show that the algorithm improves the mean average precision by 2.90 percentage points compared with existing fusion algorithms, in which the single detection precision of the car class and the pedestrian class is improved by 3.38 percentage points and 4.13 percentage points, respectively. The Mono-BEVFusion algorithm has more stable detection effect on the occluded targets or the targets with a long distance, and can effectively avoid the leakage detection phenomenon of single sensor, which has better practical application value. [ABSTRACT FROM AUTHOR]

In the context of autonomous driving, the fusion of LiDAR and camera sensors is essential for robust obstacle detection and distance estimation. However, accurately estimating the transformation matrix between cost-effective low-resolution LiDAR and cameras presents challenges due to the generation of uncertain points by low-resolution LiDAR. In the present work, a new calibration technique is developed to accurately transform low-resolution 2D LiDAR points into camera pixels by utilizing both static and dynamic calibration patterns. Initially, the key corresponding points are identified at the intersection of 2D LiDAR points and calibration patterns. Subsequently, interpolation is applied to generate additional corresponding points for estimating the homography matrix. The homography matrix is then optimized using the Levenberg–Marquardt algorithm to minimize the rotation error, followed by a Procrustes analysis to minimize the translation error. The accuracy of the developed calibration technique is validated through various experiments (varying distances and orientations). The experimental findings demonstrate that the developed calibration technique significantly reduces the mean reprojection error by 0.45 pixels, rotation error by 65.08%, and distance error by 71.93% compared to the standard homography technique. Thus, the developed calibration technique promises the accurate transformation of low-resolution LiDAR points into camera pixels, thereby contributing to improved obstacle perception in intelligent autonomous driving systems. [ABSTRACT FROM AUTHOR]

Accurate online acquisition of tool wear degradation indicators is an important prerequisite for tool wear monitoring and tool remaining life prediction. The tool degradation process is usually accompanied by the flank face of cutting tool wear and blade breakage, however, the existing degradation indicators only consider the flank face wear value, but not the tool breakage value, resulting in the lack of accuracy of degradation indicators. To this end, an online identification method of tool degradation indicators based on the fusion of image sensors and laser displacement sensors is proposed, which adopts the VGG16-UNet network to identify the wear value in the image and obtains the tool breakage value based on the time series data of the laser displacement sensor. Finally, the tool wear and breakage degradation label containing wear and breakage values is established. Compared to manual measurements, the absolute average error is within 15 µms for cutter face damage values and within 3 µms for cutter face wear values. [ABSTRACT FROM AUTHOR]

Pavement markings are a key transportation asset and traffic control device that facilitate safe and predictable driving. The effectiveness of pavement markings is dependent on their condition, particularly during nighttime and adverse weather. The Federal Highway Administration (FHWA) has developed regulations to guide minimum pavement marking retroreflectivity levels, which poses a potential challenge to public agencies because the current practice of visual inspection is labor intensive and the results can be subjective. To address the identified challenges and needs of public agencies, the objective of this research is twofold: (1) to serve as a proof of concept for the use of mobile light detection and ranging (LiDAR) to locate and assess the pavement markings for selected testing sections by developing and evaluating new automated LiDAR processing algorithms, and (2) to investigate the feasibility of identifying the deterioration trend of the retroreflectivity condition. This study developed a complete pavement marking inventory with retroreflectivity conditions for the 14 selected testing sections and also compared historical and current data to inform the deterioration trends of three types of marking materials, including polyurea, epoxy, and thermoplastic. The findings of this study will guide future phases with a larger selection of testing sections, material types, and roadway characteristics. The outcomes of the series of studies will help better define the benefit-to-cost ratio for different marking materials and eventually lead to the development of public agencies' pavement marking standards. [ABSTRACT FROM AUTHOR]

Accurate estimation of forest terrain and canopy height is crucial for timely understanding of forest growth. Gao Fen-7 (GF-7) Satellite is China's first sub-meter-level three-dimensional (3D) mapping satellite for civilian use, which was equipped with a two-line-array stereo mapping camera and a laser altimeter system that can provide stereo images and full waveform LiDAR data simultaneously. Most of the existing studies have concentrated on evaluating the accuracy of GF-7 for topographic survey in bare land, but few have in-depth studied its ability to measure forest terrain elevation and canopy height. The purpose of this study is to evaluate the potential of GF-7 LiDAR and stereo image for forest terrain and height measurement. The Airborne Laser Scanning (ALS) data were utilized to generate reference terrain and forest vertical information. The validation test was conducted in Pu'er City, Yunnan Province of China, and encouraging results have obtained. The GF-7 LiDAR data obtained the accuracy of forest terrain elevation with RMSE of 8.01 m when 21 available laser footprints were used for results verification; meanwhile, when it was used to calculate the forest height, R2 of 0.84 and RMSE of 3.2 m were obtained although only seven effective footprints were used for result verification. The canopy height values obtained from GF-7 stereo images have also been proven to have high accuracy with the resolution of 20 m × 20 m compared with ALS data (R2 = 0.88, RMSE = 2.98 m). When the results were verified at the forest sub-compartment scale that taking into account the forest types, further higher accuracy (R2 = 0.96, RMSE = 1.23 m) was obtained. These results show that GF-7 has considerable application potential in forest resources monitoring. [ABSTRACT FROM AUTHOR]

With the emergence of autonomous functions in road vehicles, there has been increased use of Advanced Driver Assistance Systems comprising various sensors to perform automated tasks. Light Detection and Ranging (LiDAR) is one of the most important types of optical sensor, detecting the positions of obstacles by representing them as clusters of points in three-dimensional space. LiDAR performance degrades significantly when a vehicle is driving in the rain as raindrops adhere to the outer surface of the sensor assembly. Performance degradation behaviors include missing points and reduced reflectivity of the points. It was found that the extent of degradation is highly dependent on the interface material properties. This subsequently affects the shapes of the adherent droplets, causing different perturbations to the optical rays. A fundamental investigation is performed on the protective polycarbonate cover of a LiDAR assembly coated with four classes of material—hydrophilic, almost-hydrophobic, hydrophobic, and superhydrophobic. Water droplets are controllably dispensed onto the cover to quantify the signal alteration due to the different droplets of various sizes and shapes. To further understand the effects of droplet motion on LiDAR signals, sliding droplet conditions are simulated using numerical analysis. The results are validated with physical optical tests, using a 905 nm laser source and receiver to mimic the LiDAR detection mechanism. Comprehensive explanations of LiDAR performance degradation in rain are presented from both material and optical perspectives. These can aid component selection and the development of signal-enhancing strategies for the integration of LiDARs into vehicle designs to minimize the impact of rain. [ABSTRACT FROM AUTHOR]

The documentation of roadway factors (such as roadway geometry, lane marking retroreflectivity/classification, and lane width) through the inventory of lane markings can reduce accidents and facilitate road safety analyses. Typically, lane marking inventory is established using either imagery or Light Detection and Ranging (LiDAR) data collected by mobile mapping systems (MMS). However, it is important to consider the strengths and weaknesses of both camera and LiDAR units when establishing lane marking inventory. Images may be susceptible to weather and lighting conditions, and lane marking might be obstructed by neighboring traffic. They also lack 3D and intensity information, although color information is available. On the other hand, LiDAR data are not affected by adverse weather and lighting conditions, and they have minimal occlusions. Moreover, LiDAR data provide 3D and intensity information. Considering the complementary characteristics of camera and LiDAR units, an image-aided LiDAR framework would be highly advantageous for lane marking inventory. In this context, an image-aided LiDAR framework means that the lane markings generated from one modality (i.e., either an image or LiDAR) are enhanced by those derived from the other one (i.e., either imagery or LiDAR). In addition, a reporting mechanism that can handle multi-modal datasets from different MMS sensors is necessary for the visualization of inventory results. This study proposes an image-aided LiDAR lane marking inventory framework that can handle up to five lanes per driving direction, as well as multiple imaging and LiDAR sensors onboard an MMS. The framework utilizes lane markings extracted from images to improve LiDAR-based extraction. Thereafter, intensity profiles and lane width estimates can be derived using the image-aided LiDAR lane markings. Finally, imagery/LiDAR data, intensity profiles, and lane width estimates can be visualized through a web portal that has been developed in this study. For the performance evaluation of the proposed framework, lane markings obtained through LiDAR-based, image-based, and image-aided LiDAR approaches are compared against manually established ones. The evaluation demonstrates that the proposed framework effectively compensates for the omission errors in the LiDAR-based extraction, as evidenced by an increase in the recall from 87.6% to 91.6%. [ABSTRACT FROM AUTHOR]

Three‐dimensional (3D) object detection, that is, localizing and classifying all critical objects in a 3D space, is essential for downstream construction scene analysis tasks. However, accurate instance segmentation, few 2D object segmentation and 3D object detection data sets, high‐quality feature representations for depth estimation, and limited 3D cues from a single red‐green‐blue (RGB) image pose significant challenges to 3D object detection and severely hinder its practical applications. In response to these challenges, an improved cascade‐based network with a transformer backbone and a boundary‐patch‐refinement method is proposed to build hierarchical features and refine object boundaries, resulting in better results in 2D object detection and instance segmentation. Furthermore, a novel self‐supervised monocular depth learning method is proposed to extract better feature representations for depth estimation from construction site video data with unknown camera parameters. Additionally, a pseudo‐LiDAR point cloud method and a 3D object detection method with a density‐based clustering algorithm are proposed to detect 3D objects in a construction scene without help from 3D labels, which will serve as a good foundation for other downstream 3D tasks. Finally, the proposed model is evaluated for object instance segmentation and depth estimation on the moving objects in construction sites (MOCS) and construction scene data sets. It brings a 9.16% gain in terms of mean average precision (mAP) for object detection and a 4.92% gain in mask mAP for object instance segmentation. The average order accuracy and relative mean error for depth estimation are improved by 0.94% and 60.56%, respectively. This study aims to overcome the challenges and limitations of 3D object detection and facilitate practical applications in construction scene analysis. [ABSTRACT FROM AUTHOR]

In the field of robotics and autonomous driving, dynamic occupancy grid maps (DOGMs) are typically used to represent the position and velocity information of objects. Although three-dimensional light detection and ranging (LiDAR) sensor-based DOGMs have been actively researched, they have limitations, as they cannot classify types of objects. Therefore, in this study, a deep learning-based camera–LiDAR sensor fusion technique is employed as input to DOGMs. Consequently, not only the position and velocity information of objects but also their class information can be updated, expanding the application areas of DOGMs. Moreover, unclassified LiDAR point measurements contribute to the formation of a map of the surrounding environment, improving the reliability of perception by registering objects that were not classified by deep learning. To achieve this, we developed update rules on the basis of the Dempster–Shafer evidence theory, incorporating class information and the uncertainty of objects occupying grid cells. Furthermore, we analyzed the accuracy of the velocity estimation using two update models. One assigns the occupancy probability only to the edges of the oriented bounding box, whereas the other assigns the occupancy probability to the entire area of the box. The performance of the developed perception technique is evaluated using the public nuScenes dataset. The developed DOGM with object class information will help autonomous vehicles to navigate in complex urban driving environments by providing them with rich information, such as the class and velocity of nearby obstacles. [ABSTRACT FROM AUTHOR]

This article addresses the challenges of measuring the 3D architecture traits, such as height and volume, of fruit tree canopies, constituting information that is essential for assessing tree growth and informing orchard management. The traditional methods are time-consuming, prompting the need for efficient alternatives. Recent advancements in unmanned aerial vehicle (UAV) technology, particularly using Light Detection and Ranging (LiDAR) and RGB cameras, have emerged as promising solutions. LiDAR offers precise 3D data but is costly and computationally intensive. RGB and photogrammetry techniques like Structure from Motion and Multi-View Stereo (SfM-MVS) can be a cost-effective alternative to LiDAR, but the computational demands still exist. This paper introduces an innovative approach using UAV-based single-lens stereoscopic photography to overcome these limitations. This method utilizes color variations in canopies and a dual-image-input network to generate a detailed canopy height map (CHM). Additionally, a block structure similarity method is presented to enhance height estimation accuracy in single-lens UAV photography. As a result, the average rates of growth in canopy height (CH), canopy volume (CV), canopy width (CW), and canopy project area (CPA) were 3.296%, 9.067%, 2.772%, and 5.541%, respectively. The r2 values of CH, CV, CW, and CPA were 0.9039, 0.9081, 0.9228, and 0.9303, respectively. In addition, compared to the commonly used SFM-MVS approach, the proposed method reduces the time cost of canopy reconstruction by 95.2% and of the cost of images needed for canopy reconstruction by 88.2%. This approach allows growers and researchers to utilize UAV-based approaches in actual orchard environments without incurring high computation costs. [ABSTRACT FROM AUTHOR]

Existing algorithms for terrain extraction from LiDAR require data to be structured in either a regular grid or triangulated irregular network (TIN) format, and they involve two-step processes; namely filtering and interpolation. This paper introduces a new terrain model based on reversing common interpolation techniques. A new method is presented to extract terrain directly through the inverse interpolation model in a single step. In addition, no raster or TIN format is required to represent the LiDAR data. Instead, points can be processed sequentially as obtained by the laser scanner. Furthermore, additional constraints for handling missing data, which occurs within waterbodies or occluded areas, are introduced in this paper. Several examples of extracted terrain in a variety of scenarios are presented and compared to results from existing algorithms both numerically and visually by inspecting vertical profiles across the data. The new terrain model was found to be in very close proximity to that obtained using existing methods but the former provided a smoother version of the terrain. [ABSTRACT FROM AUTHOR]

Multi-camera systems for structure from motion (SfM) are widely deployed in many mapping applications. Existing solutions assume known rig calibration, synchronized frames among cameras, as well as overlapping field of views (FoVs). In this paper, we derive novel geometric constraints assuming minimal knowns about the multi-camera systems, to benefit low-cost and non-expert use cases where uncalibrated multi-camera systems with non-typical geometry setups present, i.e., no rig calibration, no overlapping FoVs. Assuming that these cameras are co-located and share the same motion of the platform, the proposed constraints utilize the parallelism and length proportionality of motion vectors of these co-located cameras and formulate them as translation constraints into the bundle adjustment (BA). The proposed constraints (called motion constraints) impose a first-order penalty to co-located cameras whose motion speeds and directions between frames do not match. With soft constraints, it can handle loosely synchronized frames (with an error within one second). The proposed constraints are integrated into the BA framework and experimented with different camera setups, i.e., on a group of casually co-located GoPro cameras with no rig calibration, and some with no overlapping views. Our results show that the constraints are extremely effective in improving the reconstruction and pose accuracy for ground motion images: in our self-collected open trajectories without loop closure, the proposed constraints are effective in correcting topographical errors (i.e., trajectory drifts) of the resulting models, and the dense point clouds achieve up to 11.34 m (86.12 %) of mean absolute error (MAE) improvement as compared to reference LiDAR point clouds; our results on KITTI-odometry and KITTI-360 datasets also show an improvement of up to 28.82 m (81.05 %) in terms of the root mean square error (RMSE) of absolute pose error (APE). We expect that the proposed constraints are significant not only as additional geometric constraints for image-based mobile mapping, but also will benefit the broader use of photogrammetry, since it empowers the possibility to harness the traditionally so-called low-quality stereo/multi-camera data (e.g., by non-photogrammetry citizen scientists) into improved 3D products. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative approach to 3D environmental mapping through the integration of a compact, handheld sensor package with a two-stage sensor fusion pipeline. The sensor package, incorporating LiDAR, IMU, RGB, and thermal cameras, enables comprehensive and robust 3D mapping of various environments. By leveraging Simultaneous Localization and Mapping (SLAM) and thermal imaging, our solution offers good performance in conditions where global positioning is unavailable and in visually degraded environments. The sensor package runs a real-time LiDAR-Inertial SLAM algorithm, generating a dense point cloud map that accurately reconstructs the geometric features of the environment. Following the acquisition of that point cloud, we post-process these data by fusing them with images from the RGB and thermal cameras and produce a detailed, color-enriched 3D map that is useful and adaptable to different mission requirements. We demonstrated our system in a variety of scenarios, from indoor to outdoor conditions, and the results showcased the effectiveness and applicability of our sensor package and fusion pipeline. This system can be applied in a wide range of applications, ranging from autonomous navigation to smart agriculture, and has the potential to make a substantial benefit across diverse fields. [ABSTRACT FROM AUTHOR]

Railway track detection, which is crucial for train operational safety, faces numerous challenges such as the curved track, obstacle occlusion, and vibrations during the train's operation. Most existing methods for railway track detection use a camera or LiDAR. However, the vision-based approach lacks essential 3D environmental information about the train, while the LiDAR-based approach tends to detect tracks of insufficient length due to the inherent limitations of LiDAR. In this study, we propose a real-time method for railway track detection and 3D fitting based on camera and LiDAR fusion sensing. Semantic segmentation of the railway track in the image is performed, followed by inverse projection to obtain 3D information of the distant railway track. Then, 3D fitting is applied to the inverse projection of the railway track for track vectorization and LiDAR railway track point segmentation. The extrinsic parameters necessary for inverse projection are continuously optimized to ensure robustness against variations in extrinsic parameters during the train's operation. Experimental results show that the proposed method achieves desirable accuracy for railway track detection and 3D fitting with acceptable computational efficiency, and outperforms existing approaches based on LiDAR, camera, and camera–LiDAR fusion. To the best of our knowledge, our approach represents the first successful attempt to fuse camera and LiDAR data for real-time railway track detection and 3D fitting. [ABSTRACT FROM AUTHOR]

Photoelectric sensors based on charge‐coupled device (CCD) and complementary‐metal‐oxide‐semiconductor (CMOS) used to be planar image sensors due to the rigidity of the base materials. Moreover, they are limited to a narrow reaction wavelength range. Here, a conformal broad‐spectra laser sensor based on a flexible metal‐ferroelectric‐metal photocapacitor is promoted. The photocapacitance signal is attributed to the photothermal effect of copper (Cu) electrode and gold nanoparticles (Au NP), and the temperature‐dependent property of the dielectric constant of poly(vinylidene fluoride‐trifluoroethylene) (PVDF‐TrFE). Cu/PVDF‐TrFE/Cu and Cu/Au NP@PVDF‐TrFE/Cu flexible photocapacitors have a reliable photocapacitive response from −60 to 60 °C. Meanwhile, both photocapacitors respond from 265 to 980 nm due to the absorption of light by the composite structure, which can be used for broad‐spectra photodetection. The addition of a small amount of Au NP (0.05 vol%) effectively improves the photoresponsiveness of the device. Real‐time photodetection is realized on a hemispherical surface, and these photocapacitors with conformal characteristics are expected to gain application in an omnidirectional broad‐spectra laser alarming system. [ABSTRACT FROM AUTHOR]

The very shallow marine basin of Puck Lagoon in the southern Baltic Sea, on the Northern coast of Poland, hosts valuable benthic habitats and cultural heritage sites. These include, among others, protected Zostera marina meadows, one of the Baltic's major medieval harbours, a ship graveyard, and likely other submerged features that are yet to be discovered. Prior to this project, no comprehensive high-resolution remote sensing data were available for this area. This article describes the first Digital Elevation Models (DEMs) derived from a combination of airborne bathymetric LiDAR, multibeam echosounder, airborne photogrammetry and satellite imagery. These datasets also include multibeam echosounder backscatter and LiDAR intensity, allowing determination of the character and properties of the seafloor. Combined, these datasets are a vital resource for assessing and understanding seafloor morphology, benthic habitats, cultural heritage, and submerged landscapes. Given the significance of Puck Lagoon's hydrographical, ecological, geological, and archaeological environs, the high-resolution bathymetry, acquired by our project, can provide the foundation for sustainable management and informed decision-making for this area of interest. [ABSTRACT FROM AUTHOR]

Portable laser scanning systems (PLSSs) are a developing technology that is being used more frequently in various application domains. One notable characteristic of these systems is their capacity to gather 3D data while in motion. Additionally, unlike mobile mapping systems that are mounted to vehicles, they are particularly effective at capturing detailed information in tight, interior, and restricted spaces. This article presents two typical applications of this promising technology for modelling huge buildings. The purpose is to equip readers with fundamental information and comprehension of the capabilities and obstacles associated with this technology. A comparison is made of two methods of interior spatial data collection with lidar technology - ground-based laser scanning with a Trimble TX6 and a handheld GeoSLAM ZEB Horizon device. The technological process for creating a 3D model of two large buildings for future reconstruction is described. [ABSTRACT FROM AUTHOR]

With the escalating demand for efficient traffic management and the increasing complexity of traffic control, diverse sensor technologies have been implemented to measure traffic in real-time. The road-side LiDAR emerges as a novel technology addressing the data gap in multimodal traffic analyses. LiDAR sensing return time to precisely capture distance and reflectivity, generating point cloud data encompassing all traffic trajectory information. It overcomes challenges posed by illumination conditions like light, dust and fog, which often affect camera sensor performance. In addition, LiDAR sensing minimises the effect of changing object position and angles, simplifying object detection and recognition.This paper tackles the challenges of analysing LiDAR-derived traffic data by proposing a method for traffic trajectory data enrichment. The methodology followed includes creating a semantic map, bridging the physical space and raw data, transforming from a local to a standard Coordinate Reference System (CRS) and enriching data trajectory representation. Three use cases are presented based on the dataset obtained after enrichment: object classification, permissible directions violation detection, and traffic flow density. The proposed method is validated using traffic data from a LiDAR system of 6 sensors located in one of the busiest intersections in Sofia, Bulgaria. The raw sensor data is processed by a fusion box called the Augmented LiDAR Box, delivering time series frames with labelled moving objects in.osef format. The results prove that the proposed data enrichment method successfully transforms the trajectories into semantic sequences, opening up new avenues for trajectory analysis and intersection traffic micro-modelling. [ABSTRACT FROM AUTHOR]

We assess the temperature stability requirements of unseeded Nd:YAG lasers in lidar systems for atmospheric temperature profiling through the rotational Raman technique. Taking as a reference a system using a seeded laser assumed to emit pulses of negligible spectral width and wavelength-drift free, we estimate first the effect of the pulse spectral widening of the unseeded laser on the output of the interference filters, then we derive the limits of the allowable wavelength drift for a given bias in the temperature measurement that would add to the noise-induced uncertainty. Finally, using spectroscopic data, we relate the allowable wavelength drift to allowable temperature variations of the YAG rod. We find that, in order to keep the bias affecting atmospheric temperature measurements smaller than 1 K, the Nd:YAG rod temperature should also be kept within 1 K. [ABSTRACT FROM AUTHOR]

Wood accumulations influence geomorphic, hydraulic, and ecologic functions within a river corridor, but characterizing these accumulations presents challenges across a range of field and remote sensing methodologies. We evaluate the ability of handheld lidar scanners, specifically lidar‐scanning capabilities of a fourth‐generation iPad Pro, to collect three‐dimensional wood accumulation data, which can be used to inform measurements of wood volume, porosity, complexity, and roughness. We discuss the potential and limitations of this novel methodology for river research and management. We found that handheld lidar presents a cost‐effective input for data‐processing workflows that field measurements of wood accumulation dimensions cannot as easily replicate including (1) a user‐friendly means of data collection and visualization; (2) accurate comparisons of wood volume over time; (3) integration into workflows to measure porosity parameters; and (4) potential use in informing hydraulic and morphodynamic models. Consideration of study area constraints and intended use of scans are prerequisites to using handheld lidar as an effective tool. We identified some specific limitations of using handheld lidar scanners in wood‐rich river corridors, including (1) scanners perform poorly when wood is under water or surrounded by dense vegetation; (2) scanners require physical access to areas of interest at distances less than 5 m; (3) scans need to be manually georeferenced; and (4) scans require manual measurements for any dimensional data, which still have associated user time and error. Handheld lidar as a scientific tool is rapidly developing and there is substantial room for expansion of applications, utilization, and advances in the use of this tool in river research and management. [ABSTRACT FROM AUTHOR]

Aerial mapping using Unmanned Aerial Systems (UAS), such as the DJI Mavic 3 Enterprise, has revolutionized photogrammetry, enabling efficient data capture for small-scale projects. The typical nadir perspective of UAS mapping, however, imposes limitations on capturing critical details of features due to its predominantly vertical viewpoint. Overcoming this challenge often requires manual, low-altitude flights by experienced UAS pilots to achieve high-angle oblique perspectives, unless gimbled camera mount is used. This study explores the integration of high oblique angle perspectives using the iPhone 15 Pro, which boasts advanced camera capabilities and an integrated LiDAR sensor, to complement UAS imagery. The iPhone 15 Pro's camera sensors provide a Ground Sampled Distance (GSD) comparable to UAS cameras, while its LiDAR sensor, with about five meters of range, enhances mapping capabilities by delivering accurate depth measurements in close range. By utilizing various georeferencing options for the imagery and LiDAR data from the iPhone 15 Pro with UAS nadir imagery, we can achieve a more comprehensive object space reconstruction, significantly improving the accuracy of geospatial mapping. Both the Mavic 3 Enterprise and the iPhone 15 Pro, though operating independently on their respective platforms, support Real-Time Kinematic (RTK) corrections, facilitating precise positioning for the entire system trajectory. Strategic placement and utilization of Ground Control Points (GCPs) aid in the georeferencing of the complete dataset, enhancing its overall accuracy. To validate the accuracy of the acquired data, checkpoints are established on-site. The positions derived from the integrated UAS and iPhone 15 Pro data are compared against these checkpoints to quantify the accuracy and reliability of the data. Additionally, Post-Processed Kinematic (PPK) techniques are employed to validate the trajectories of all data collection systems, ensuring the reliability of the acquired data, especially in instances where RTK corrections may be lacking. In summary, this research showcases comprehensive, multi-dimensional geospatial datasets by conducting validation studies that assess the accuracy and reliability of georeferenced datasets against known ground truth checkpoints. Such validation studies are crucial for identifying gaps in current methodologies and suggesting areas for improvement, thereby aiming to enhance the quality and accuracy of geospatial mapping applications. Through the integration of UAS and smartphone mapping, complemented by rigorous validation efforts, we aspire to achieve improved geospatial mapping accuracy. [ABSTRACT FROM AUTHOR]

Estimating forest canopy height is crucial for assessing aboveground biomass and carbon sequestration. Light detection and ranging (lidar) is an important technology for its ability in capturing vertical structural information. However, due to instrument limitations and cost constraints, acquiring large-scale and continuous forest data solely through lidar is challenging. To compensate this, remote sensing images can be used to cover wide regions. Therefore, leveraging multi-source data for constructing canopy height models (CHMs) holds great promise in this field. The objective of this study is to evaluate and compare the contributions of multi-source remote sensing data and methods in estimating forest canopy height. In constructing the CHM, the commonly used random forest (RF) and fully convolutional network (FCN) are assessed. The canopy height obtained from GEDI was used as the reference data, and Landsat 8 and Sentinel-2 data were used for prediction. Multiple CHMs were constructed for the Dabie Mountains, Central China, in 2019 based on different data sources and methods, respectively, which are then comparatively analysed. The results showed that (1) the accuracy of the CHM using Sentinel-2 as input is marginally better than that using Landsat 8 based on RF, where the difference is insignificant; and (2) FCN is less accurate than RF despite domain-specific fine-tuning, although further improvement in accuracy is expected by weighing in more FCN models. [ABSTRACT FROM AUTHOR]

In light of the limited efficacy of conventional methods for identifying pavement cracks and the absence of comprehensive depth and location data in two-dimensional photographs, this study presents an intelligent strategy for extracting road cracks. This methodology involves the integration of laser point cloud data obtained from a vehicle-mounted system and a panoramic sequence of images. The study employs a vehicle-mounted LiDAR measurement system to acquire laser point cloud and panoramic sequence image data simultaneously. A convolutional neural network is utilized to extract cracks from the panoramic sequence image. The extracted sequence image is then aligned with the laser point cloud, enabling the assignment of RGB information to the vehicle-mounted three dimensional (3D) point cloud and location information to the two dimensional (2D) panoramic image. Additionally, a threshold value is set based on the crack elevation change to extract the aligned roadway point cloud. The three-dimensional data pertaining to the cracks can be acquired. The experimental findings demonstrate that the use of convolutional neural networks has yielded noteworthy outcomes in the extraction of road cracks. The utilization of point cloud and image alignment techniques enables the extraction of precise location data pertaining to road cracks. This approach exhibits superior accuracy when compared to conventional methods. Moreover, it facilitates rapid and accurate identification and localization of road cracks, thereby playing a crucial role in ensuring road maintenance and traffic safety. Consequently, this technique finds extensive application in the domains of intelligent transportation and urbanization development. The technology exhibits significant promise for use in the domains of intelligent transportation and city development. [ABSTRACT FROM AUTHOR]

In this paper a statistical study of cirrus geometrical and optical properties based on 4 years of continuous ground-based lidar measurements with the Barcelona (Spain) Micro Pulse Lidar (MPL) is analysed. First, a review of the literature on the two-way transmittance method is presented. This method is a well-known lidar inversion method used to retrieve the optical properties of an aerosol–cloud layer between two molecular (i.e. aerosol and cloud-free) regions below and above, without the need to make any a priori assumptions about their optical and/or microphysical properties. Second, a simple mathematical expression of the two-way transmittance method is proposed for both ground-based and spaceborne lidar systems. This approach of the method allows the retrieval of the cloud optical depth, the cloud column lidar ratio and the vertical profile of the cloud backscatter coefficient. The method is illustrated for a cirrus cloud using measurements from the ground-based MPL and from the spaceborne Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). Third, the database is then filtered with a cirrus identification criterion based on (and compared to) the literature using only lidar and radiosonde data. During the period from November 2018 to September 2022, 367 high-altitude cirrus clouds were identified at 00:00 and 12:00 UTC, of which 203 were successfully inverted with the two-way transmittance method. The statistical results of these 203 high-altitude cirrus clouds show that the cloud thickness is 1.8 ± 1.1 km, the mid-cloud temperature is - 51 ± 8 ∘ C and the linear cloud depolarization ratio is 0.32 ± 0.13. The application of the transmittance method yields an average cloud optical depth (COD) of 0.36 ± 0.45 and a mean effective column lidar ratio of 30 ± 19 sr. Statistical results of the errors associated with the two-way transmittance method retrievals are also provided. The highest occurrence of cirrus is observed in spring and the majority of cirrus clouds (48 %) are visible (0.03 < COD < 0.3), followed by opaque (COD > 0.3) with a percentage of 38 %. Together with results from other sites, possible latitudinal dependencies have been analysed together with correlations between cirrus cloud properties. For example, we noted that in Barcelona the COD correlates positively with the cloud base temperature, effective column lidar ratio and linear cloud depolarization ratio and negatively with the cloud base height. [ABSTRACT FROM AUTHOR]

Object detection is a key task in automatic driving, and the poor performance of small object detection is a challenge that needs to be overcome. Previously, object detection networks could detect large-scale objects in ideal environments, but detecting small objects was very difficult. To address this problem, we propose a multi-layer fusion 3D object detection network. First, a dense fusion (D-fusion) method is proposed, which is different from the traditional fusion method. By fusing the feature maps of each layer, more semantic information of the fusion network can be preserved. Secondly, in order to preserve small objects at the feature map level, we designed a feature extractor with an adaptive fusion module (AFM), which reduces the impact of the background on small objects by weighting and fusing different feature layers. Finally, an attention mechanism was added to the feature extractor to accelerate the training efficiency and convergence speed of the network by suppressing information that is irrelevant to the task. The experimental results show that our proposed approach greatly improves the baseline and outperforms most state-of-the-art methods on KITTI object detection benchmarks. [ABSTRACT FROM AUTHOR]

LiDAR sensors, pivotal in various fields like agriculture and robotics for tasks such as 3D object detection and map creation, are increasingly coupled with thermal cameras to harness heat information. This combination proves particularly effective in adverse conditions like darkness and rain. Ensuring seamless fusion between the sensors necessitates precise extrinsic calibration. Our innovative calibration method leverages human presence during sensor setup movements, eliminating the reliance on dedicated calibration targets. It optimizes extrinsic parameters by employing a novel evolutionary algorithm on a specifically designed loss function that measures human alignment across modalities. Our approach showcases a notable 4.43 % improvement in the loss over extrinsic parameters obtained from target-based calibration in the FieldSAFE dataset. This advancement reduces costs related to target creation, saves time in diverse pose collection, mitigates repetitive calibration efforts amid sensor drift or setting changes, and broadens accessibility by obviating the need for specific targets. The adaptability of our method in various environments, like urban streets or expansive farm fields, stems from leveraging the ubiquitous presence of humans. Our method presents an efficient, cost-effective, and readily applicable means of extrinsic calibration, enhancing sensor fusion capabilities in the critical fields reliant on precise and robust data acquisition. [ABSTRACT FROM AUTHOR]

We aimed to improve the detection accuracy of laser methane sensors in expansive temperature application environments. In this paper, a large-scale dataset of the measured concentration of the sensor at different temperatures is established, and a temperature compensation model based on the ISSA-BP neural network is proposed. On the data side, a large-scale dataset of 15,810 sets of laser methane sensors with different temperatures and concentrations was established, and an Improved Isolation Forest algorithm was used to clean the large-scale data and remove the outliers in the dataset. On the modeling framework, a temperature compensation model based on the ISSA-BP neural network is proposed. The quasi-reflective learning, chameleon swarm algorithm, Lévy flight, and artificial rabbits optimization are utilized to improve the initialization of the sparrow population, explorer position, anti-predator position, and position of individual sparrows in each generation, respectively, to improve the global optimization seeking ability of the standard sparrow search algorithm. The ISSA-BP temperature compensation model far outperforms the four models, SVM, RF, BP, and PSO-BP, in model evaluation metrics such as MAE, MAPE, RMSE, and R-square for both the training and test sets. The results show that the algorithm in this paper can significantly improve the detection accuracy of the laser methane sensor under the wide temperature application environment. [ABSTRACT FROM AUTHOR]

This paper proposes a novel camera–LiDAR calibration method that utilizes an iterative random sampling and intersection line-based quality evaluation using a foldable plane pair. Firstly, this paper suggests using a calibration object consisting of two small planes with ChArUco patterns, which is easy to make and convenient to carry. Secondly, the proposed method adopts an iterative random sampling to make the calibration procedure robust against sensor data noise and incorrect object recognition. Lastly, this paper proposes a novel quality evaluation method based on the dissimilarity between two intersection lines of the plane pairs from the two sensors. Thus, the proposed method repeats random sampling of sensor data, extrinsic parameter estimation, and quality evaluation of the estimation result in order to determine the most appropriate calibration result. Furthermore, this method can also be used for the LiDAR–LiDAR calibration with a slight modification. In experiments, the proposed method was quantitively evaluated using simulation data and qualitatively assessed using real-world data. The experimental results show that the proposed method can successfully perform both camera–LiDAR and LiDAR–LiDAR calibrations while outperforming the previous approaches. [ABSTRACT FROM AUTHOR]

Forest scene 3D reconstruction serves as the fundamental basis for crucial applications such as forest resource inventory, forestry 3D visualization, and the perceptual capabilities of intelligent forestry robots in operational environments. However, traditional 3D reconstruction methods like LiDAR present challenges primarily because of their lack of portability. Additionally, they encounter complexities related to feature point extraction and matching within multi-view stereo vision sensors. In this research, we propose a new method that not only reconstructs the forest environment but also performs a more detailed tree reconstruction in the scene using conditional generative adversarial networks (CGANs) based on a single RGB image. Firstly, we introduced a depth estimation network based on a CGAN. This network aims to reconstruct forest scenes from images and has demonstrated remarkable performance in accurately reconstructing intricate outdoor environments. Subsequently, we designed a new tree silhouette depth map to represent the tree's shape as derived from the tree prediction network. This network aims to accomplish a detailed 3D reconstruction of individual trees masked by instance segmentation. Our approach underwent validation using the Cityscapes and Make3D outdoor datasets and exhibited exceptional performance compared with state-of-the-art methods, such as GCNDepth. It achieved a relative error as low as 8% (with an absolute error of 1.76 cm) in estimating diameter at breast height (DBH). Remarkably, our method outperforms existing approaches for single-image reconstruction. It stands as a cost-effective and user-friendly alternative to conventional forest survey methods like LiDAR and SFM techniques. The significance of our method lies in its contribution to technical support, enabling the efficient and detailed utilization of 3D forest scene reconstruction for various applications. [ABSTRACT FROM AUTHOR]

Light Detection and Ranging (LiDAR) commonly uses fiber optic as the transmission medium because it can monitor over long distances, has strong resistance from extreme environments, and has anti-electromagnetic interference. According to previous research, LiDAR characteristics related to the effect of fiber optic length on FMCS-based LiDAR applications have not been analyzed. The novelty of this research is that it uses a distance sensor designed using the FMCW-based LiDAR method. Therefore, this study in FMCW-based LiDAR was carried out to determine the characteristics of optical fiber in length variations. Analysis was obtained by comparing the effect of optical fiber length on attenuation through power loss and time delay during the transmission process. When the time delay occurred, the photodetector transmitted and reflected the beam. Fiber optic length is exponentially related to attenuation with y = 0.2064e0.0073x. Meanwhile, the time delay is linearly proportional with y = 69.849x + 9.149. Besides, the study also gets the effect of optical fiber absorption, connection, bending, lens attenuation, free space, and refractive index homogeneity that can be used as the basis for the following LiDAR technology development. [ABSTRACT FROM AUTHOR]

Next‐generation lunar laser ranging (LLR) aims for mm‐level accuracy, significantly promoting the study of gravitational physics. However, atmospheric refraction delay causes an unacceptable ranging error that can be efficiently corrected by neither the atmospheric delay models nor ray‐tracing calculation. Here we report on an improved ray‐tracing method, in which temperature profiles measured by radiosonde are replaced by those measured by a pure rotational Raman lidar, to realize real‐time and co‐located delay correction. Using 9‐night lidar observations, temperature‐induced delays of ±15 mm can be corrected for laser ranging, which is the largest residual in the LLR error budget. Plain Language Summary: Lunar laser ranging (LLR) measures the distance between ranging station and corner cube retroreflector arrays on the Moon to study the Earth‐Moon system and test Einstein's general relativity theory. The accuracy of LLR directly determines how accurately these scientific issues can be understood. Next‐generation LLR aiming at millimeter‐level ranging accuracy is confronted with a shortcoming caused by atmospheric refractive index delay, which cannot be efficiently corrected by either the atmospheric delay models or traditional ray‐tracing calculation. To eliminate one of the most important error sources, that is, temperature, we improve the ray‐tracing calculation by replacing the radiosonde‐measured temperature profiles with those measured by a pure rotational Raman lidar, to realize real‐time and co‐located delay correction. According to the lidar observations during nine nights, it is found that temperature‐induced delays of ±15 mm can be well corrected for laser ranging, which is considered the first essential step toward millimeter‐level LLR. Key Points: The accuracy of atmospheric delay correction with M‐P model are generally within ±5 mm in WuhanWe propose to install a pure rotational Raman lidar beside the lunar laser ranging system to measure the simultaneous and collocated temperature profilesFor 9‐night observations, lidar‐measured temperature profiles correct the model‐ and radiosonde‐derived delays of ±15 mm [ABSTRACT FROM AUTHOR]

Monocular depth estimation is a task aimed at predicting pixel-level distances from a single RGB image. This task holds significance in various applications including autonomous driving and robotics. In particular, the recognition of surrounding environments is important to avoid collisions during autonomous parking. Fisheye cameras are adequate to acquire visual information from a wide field of view, reducing blind spots and preventing potential collisions. While there have been increasing demands for fisheye cameras in visual-recognition systems, existing research on depth estimation has primarily focused on pinhole camera images. Moreover, depth estimation from fisheye images poses additional challenges due to strong distortion and the lack of public datasets. In this work, we propose a novel underground parking lot dataset called JBNU-Depth360, which consists of fisheye camera images and their corresponding LiDAR projections. Our proposed dataset was composed of 4221 pairs of fisheye images and their corresponding LiDAR point clouds, which were obtained from six driving sequences. Furthermore, we employed a knowledge-distillation technique to improve the performance of the state-of-the-art depth-estimation models. The teacher–student learning framework allows the neural network to leverage the information in dense depth predictions and sparse LiDAR projections. Experiments were conducted on the KITTI-360 and JBNU-Depth360 datasets for analyzing the performance of existing depth-estimation models on fisheye camera images. By utilizing the self-distillation technique, the AbsRel and SILog error metrics were reduced by 1.81% and 1.55% on the JBNU-Depth360 dataset. The experimental results demonstrated that the self-distillation technique is beneficial to improve the performance of depth-estimation models. [ABSTRACT FROM AUTHOR]

In the past decade, the automotive light detection and ranging (LiDAR) has been experiencing a rapid expansion stage. Many researchers have been involved in the research of LiDARs and have installed it in vehicles as a means of enhancing autopilot capabilities. Compared with a traditional millimeter wave radar, LiDARs have many advantages such as the high imaging resolution, long measurement range, and the ability to reconstruct 3D information around the vehicle. These features make LiDARs one of the crucial research hotspots in the field of autopilot. The basic principles of LiDARs are the same as those of a laser rangefinder. The distance information can be obtained by locating the echo instant corresponding to the laser emission moment. But if the interval between two adjacent laser pulses is extremely narrow, the regions of the light emission and echo will be overlapped. Therefore, a range ambiguity will occur and the distance information calculation process will become abnormal. Besides, the high resolution of LiDARs is also characterized by its extremely high emissions frequency. Whilst the information about the surrounding environment of an automotive car can be retrieved more accurately, it means that the possibility of range ambiguity is also increasing at the same time. In this paper, we propose an algorithm for solving the range ambiguity problem of the LiDARs based on the concept of classification and can be accelerated by the FPGA approach, for the first time in the field of an automotive LiDAR. The algorithm can be performed by employing a single wavelength pulsed laser and can be specifically optimized for the demands of field programmable gate arrays (FPGAs). While guaranteeing the high resolution of LiDARs, the attenuation of the measurement ability should exceed due to the occurrence of range ambiguity. It can also match the demand for the processing speed of large amounts of point cloud information data. Through controlling the cost of the whole device, the performance of the LiDAR can be greatly improved. The result of this paper might provide a bright future of automotive LiDARs with the high data processing efficiency and the high resolution at the same time. [ABSTRACT FROM AUTHOR]

This study presents a methodology for the coarse alignment of light detection and ranging (LiDAR) point clouds, which involves estimating the position and orientation of each station using the pinhole camera model and a position/orientation estimation algorithm. Ground control points are obtained using LiDAR camera images and the point clouds are obtained from the reference station. The estimated position and orientation vectors are used for point cloud registration. To evaluate the accuracy of the results, the positions of the LiDAR and the target were measured using a total station, and a comparison was carried out with the results of semi-automatic registration. The proposed methodology yielded an estimated mean LiDAR position error of 0.072 m, which was similar to the semi-automatic registration value of 0.070 m. When the point clouds of each station were registered using the estimated values, the mean registration accuracy was 0.124 m, while the semi-automatic registration accuracy was 0.072 m. The high accuracy of semi-automatic registration is due to its capability for performing both coarse alignment and refined registration. The comparison between the point cloud with refined alignment using the proposed methodology and the point-to-point distance analysis revealed that the average distance was measured at 0.0117 m. Moreover, 99% of the points exhibited distances within the range of 0.0696 m. [ABSTRACT FROM AUTHOR]

The development of high-precision satellites has increased the demand for ultraprecision three-degrees-of-freedom (3-DOF) angle measurements for detecting structural deformation. The required instrument should simultaneously measure pitch, yaw, and roll angles using a single reference point. This paper proposes a 3-DOF angle measurement method based on the wavefront interference principle, and a mathematical model and its decoupling algorithm were built. Then, an angle-sensing probe with an extremely simple structure was designed and constructed. Finally, a series of experiments were performed to verify the method's feasibility. The experiment results showed that the roll, pitch, and yaw measurement resolution of the probe was better than 10, 1, and 1 μrad, respectively, providing a high-performance 3-DOF angle measurement with a single probe. The short-term stabilities of roll, pitch, and yaw were better than 22, 1.7, and 2.0 μrad, respectively. [ABSTRACT FROM AUTHOR]

Self-driving vehicles demand efficient and reliable depth-sensing technologies. Lidar, with its capability for long-distance, high-precision measurement, is a crucial component in this pursuit. However, conventional mechanical scanning implementations suffer from reliability, cost, and frame rate limitations. Solid-state lidar solutions have emerged as a promising alternative, but the vast amount of photon data processed and stored using conventional direct time-of-flight (dToF) prevents long-distance sensing unless power-intensive partial histogram approaches are used. In this paper, we introduce a groundbreaking 'guided' dToF approach, harnessing external guidance from other onboard sensors to narrow down the depth search space for a power and data-efficient solution. This approach centers around a dToF sensor in which the exposed time window of independent pixels can be dynamically adjusted. We utilize a 64-by-32 macropixel dToF sensor and a pair of vision cameras to provide the guiding depth estimates. Our demonstrator captures a dynamic outdoor scene at 3 fps with distances up to 75 m. Compared to a conventional full histogram approach, on-chip data is reduced by over twenty times, while the total laser cycles in each frame are reduced by at least six times compared to any partial histogram approach. The capability of guided dToF to mitigate multipath reflections is also demonstrated. For self-driving vehicles where a wealth of sensor data is already available, guided dToF opens new possibilities for efficient solid-state lidar. [ABSTRACT FROM AUTHOR]