Ground Points Research Articles

A Ground Point Fitting Method for Winter Wheat Height Estimation Using UAV-Based SfM Point Cloud Data

Height is a key factor in monitoring the growth status and rate of crops. Compared with large-scale satellite remote sensing images and high-cost LiDAR point cloud, the point cloud generated by the Structure from Motion (SfM) algorithm based on UAV images can quickly estimate crop height in the target area at a lower cost. However, crop leaves gradually start to cover the ground from the beginning of the stem elongation stage, making more and more ground points below the canopy disappear in the data. The terrain undulations and outliers will seriously affect the height estimation accuracy. This paper proposed a ground point fitting method to estimate the height of winter wheat based on the UAV SfM point cloud. A canopy slice filter was designed to reduce the interference of middle canopy points and outliers. Random Sample Consensus (RANSAC) was applied to obtain the ground points from the valid filtered point cloud. Then, the missing ground points were fitted according to the known ground points. Furthermore, we achieved crop height monitoring at the stem elongation stage with an R2 of 0.90. The relative root mean squared error (RRMSE) of height estimation was 5.9%, and the relative mean absolute error (RMAE) was 4.6% at the stem elongation stage. This paper proposed the canopy slice filter and fitting missing ground points. It was concluded that the canopy slice filter successfully optimized the extraction of ground points and removed outliers. Fitting the missing ground points simulated the terrain undulations effectively and improved the accuracy.

A Triangular Grid Filter Method Based on the Slope Filter

High-precision ground point cloud data has a wide range of applications in various fields, and the separation of ground points from non-ground points is a crucial preprocessing step. Therefore, designing an efficient, accurate, and stable ground extraction algorithm is highly significant for improving the processing efficiency and analysis accuracy of point cloud data. The study area in this article was a park in Guilin, Guangxi, China. The point cloud was obtained by utilizing the UAV platform. In order to improve the stability and accuracy of the filter algorithm, this article proposed a triangular grid filter based on the Slope Filter, found violation points by the spatial position relationship within each point in the triangulation network, improved KD-Tree-Based Euclidean Clustering, and applied it to the non-ground point extraction. This method is accurate, stable, and achieves the separation of ground points from non-ground points. Firstly, the Slope Filter is used to remove some non-ground points and reduce the error of taking ground points as non-ground points. Secondly, a triangular grid based on the triangular relationship between each point is established, and the violation triangle is determined through the grid; thus, the corresponding violation points are found in the violation triangle. Thirdly, according to the three-point collinear method to extract the regular points, these points are used to extract the regular landmarks by the KD-Tree-Based Euclidean Clustering and Convex Hull Algorithm. Finally, the dispersed points and irregular landmarks are removed by the Clustering Algorithm. In order to confirm the superiority of this algorithm, this article compared the filter effects of various algorithms on the study area and filtered the 15 data samples provided by ISPRS, obtaining an average error of 3.46%. The results show that the algorithm presented in this article has high processing efficiency and accuracy, which can significantly improve the processing efficiency of point cloud data in practical applications.

The March 1940 Superstorm: Geoelectromagnetic Hazards and Impacts on American Communication and Power Systems

AbstractAn analysis is made of geophysical records of the 24 March 1940, magnetic storm and related reports of interference on long‐line communication and power systems across the contiguous United States and, to a lesser extent, Canada. Most long‐line system interference occurred during local daytime, after the second of two storm sudden commencements and during the early part of the storm's main phase. The high degree of system interference experienced during this storm is inferred to have been due to unusually large‐amplitude and unusually rapid geomagnetic field variation, possibly driven by interacting interplanetary coronal‐mass ejections. Geomagnetic field variation, in turn, induced geoelectric fields in the electrically conducting solid Earth, establishing large potential differences (voltages) between grounding points at communication depots and transformer substations connected by long transmission lines. It is shown that March 1940 storm‐time communication‐ and power‐system interference was primarily experienced over regions of high electromagnetic surface impedance, mainly in the upper Midwest and eastern United States. Potential differences measured on several grounded long lines during the storm exceeded 1‐min resolution voltages that would have been induced by the March 1989 storm. In some places, voltages exceeded American electric‐power‐industry benchmarks. It is concluded that the March 1940 magnetic storm was unusually effective at inducing geoelectric fields. Although modern communication systems are now much less dependent on long electrically conducting transmission lines, modern electric‐power‐transmission systems are more dependent on such lines, and they, thus, might experience interference with the future occurrence of a storm as effective as that of March 1940.

A low‐drift and real‐time localisation and mapping method for handheld hemispherical view LiDAR‐IMU integration system

AbstractThis paper proposes a simultaneous localisation and mapping (SLAM) framework that uses a handheld hemispherical view LiDAR‐IMU integration system. Inspired by the specific characteristic of the hemispherical view LiDAR, a ground segmentation module based on seed points is designed. The ground points are then downsampled to eliminate redundant vertical constraints. The IMU data and the pre‐processed point cloud are used to perform state estimation via a tightly coupled iterative extended Kalman filter (iEKF) to obtain the pose estimation. The automatically detected loop closures provide closed‐loop constraints for the odometry, and a factor graph ensures the global consistency of the map. Data from diverse scenes are collected via a prototype system. Both qualitative and quantitative experiments are carried out to verify the framework's performance. According to the experimental results, our framework achieves low‐drift, high‐coverage and real‐time performance, outperforming the state‐of‐the‐art LiDAR SLAM methods in our handheld hemispherical view LiDAR‐IMU test sites. For the research community's benefit, the dataset is publicly provided for other researchers to compare against.

Inclusive curatorial strategies: A case study of an interdisciplinary conference and festival: Communities and Communication

The article explores the curatorial strategies involved in the completion of an interdisciplinary conference. Considering factors of control, space, opportunity and most importantly generating a safe space to experiment and participate in an academic conference to those generally seen as outside the academy. With this in mind the work explores the ways in which the event became a testing ground and developmental jumping off point to identify the ways in which support can be offered to a widened participant pool.

Adaptive random sample consensus method for ground filtering of airborne LiDAR

Ground filtering is an essential step in the comprehensive processing of airborne LiDAR point clouds. However, the performances of existing ground filtering algorithms are usually affected by manual thresholds, and many algorithms have high complexity and are not suitable for applications with high timeliness requirements. In this paper, a fast ground filtering algorithm for airborne point clouds based on Random Sample Consensus (RANSAC) and adaptive threshold acquisition is proposed. Statistical filtering algorithm is used to filter out outliers and abnormal points based on the Z-scale sequence of the point clouds, the threshold is adaptively obtained according to the filtered Z-scale sequence, and then the adaptive threshold and RANSAC are combined to achieve rapid ground filtering. The proposed algorithm is used to perform threshold calculation and ground filtering on point clouds dataset and real-world point clouds, the results show that the adaptively obtained threshold is located in the optimal threshold interval, and the algorithm in this paper can extract ground points quickly and with small errors. The proposed method provides a reference algorithm for the airborne point clouds processing field that requires high timeliness, such as reconnaissance and Strike Integrated UAV, ground target identification and tracking, etc.

High Accuracy and Low Complexity Lidar Place Recognition Using Unitary Invariant Frobenius Norm

Simultaneous Localization and Mapping (SLAM) is used in solving the problems of localization, navigation, and map construction for autonomous vehicles moving in unknown environments. Place recognition is an inevitable subject in SLAM, and the current lidar-based methods have been popularized for their rising environmental robustness. Currently, the place recognition method with lidar has attracted much attention due to its high environmental robustness. However, many 3D laser point cloud methods for place recognition descriptors construction with the local or global point-cloud data omitting some intrinsic properties of the point clouds. In this article, we propose a place recognition method based on the unitary invariance of the Frobenius norm for utilizing different attributes of ground points and non-ground points. Specifically, the interpretable filtering framework and a dynamic threshold adjustment strategy are raised according to different environments and sensors with intensity and geometric information. As commissioning, the comparative experiments are conducted with CHDloop datasets and public KITTI datasets of different scales and access types. Compared to the original Scan Context (SC) and Intensity Scan Context (ISC) methods, our proposed method achieves higher efficiency while maintaining a improved recall rate and precision. This novel method has been integrated into the existing LiDAR SLAM and formed a new framework FSC_ALOAM that reduces drifts in point-cloud mapping. The entire process improves the accordance of maps at the identical position in auto-driving.

PPK PROCESSING TO BOOST UAS ACCURACY IN CADASTRAL MAPPING

Abstract. Unmanned Aerial Systems (UAS) are increasingly used in different applications, including 3D urban modelling, cadastral mapping, urban planning, GIS information system and other fields because of their advantages. As a consequence, UAS equipment is constantly developed to provide more accurate results in a more reliable mode. This paper aims to evaluate the performances of a low-cost UAS system, namely DJI Phantom 4 Pro v2 equipped with a TeoKIT GNSS PPK (post-processing kinematic) module for cadastral mapping purposes. Two fights (oblique and nadir) over a residential area at 60 m height were performed and some 100 ground points were used to derive RMSE accuracies. Comparison between GNSS-aided with PPK processing and indirect georeferencing processes are performed. Given a mobile laser scanner (MLS) point cloud as ground truth, comparison with UAS point clouds and manually digitized features are also performed and reported.

Design analysis and control of step‐up converter with reduced switch stress

SummaryIn light of the increasing share of renewable energy sources (RES) in electricity generation, the research community is directing their attention toward developing highly efficient DC‐DC high‐gain step‐up converters. These converters are designed to exhibit reduced voltage stress on the switching devices (semiconductors). This paper presents a comprehensive study of a switch voltage stress‐reduced step‐up converter that offers a range of additional features, including the availability of a common ground point, continuous input current, simple two‐mode continuous conduction mode (CCM) operation, wide duty cycle range, and quadratic boosting factor. To achieve the aforementioned features, the converter utilizes two switches with the same operating modes, eliminating the need for complex switching circuitry. Operational modes, steady‐state analysis, and design considerations have been presented in detail. Comparative analysis indicates that the voltage conversion ratio per number of devices in the proposed topology is significantly higher than that of similar topologies. In addition, the voltage stress of switches is considerably less than the topologies addressed in the literature. The study further includes detailed mathematical and experimental analyses, small signal analysis, and hardware implementation to support the theoretical findings. The converter's efficiency has been thoroughly analyzed along with thermal analysis to ensure its reliability.

Robust registration of aerial and close‐range photogrammetric point clouds using visual context features and scale consistency

AbstractPoint cloud registration is of great significance to the reconstruction of high‐precision 3D city models. There are some challenges when aligning aerial and close‐range photogrammetric point clouds, such as huge view differences caused by the different sights of the sensors, massive noisy points due to the error in dense matching, and scale uncertainty since there are no control points for absolute orientation. To achieve complementary advantages of aerial and close‐range point clouds, in this paper, a robust cross‐source point clouds registration method is proposed using image visual context features and scale consistency. First, a cross‐view image matching method based on image visual context features is proposed to obtain corresponding points. Second, to overcome the challenges of noisy points and scale differences, an outlier filtering method is designed based on scale consistency. Finally, the dual quaternions model considering the scale factor is introduced to solve the spatial transformation model. To analyze the feasibility of this method qualitatively and quantitatively, experiments are conducted using the public scene dataset of Dortmund, Germany, and the scene dataset of Zhengzhou City, China. Three kinds of cross‐source point clouds registration experiments are conducted in this paper, including aerial and close‐range point clouds registration in Dortmund, and aerial and ground point clouds registrations in both Dortmund and Zhengzhou. The chamfer distances of the three sets of experiments are 4.48 m, 5.97 m and 4.78 m, respectively. The ablation study shows that the outlier filtering method and the dual quaternions model improve the accuracy by at least 14% and 25%, respectively. The experiments demonstrate that the method accomplishes the cross‐source point clouds registration in large‐scale scenes accurately and efficiently, providing a solid foundation for subsequent fine 3D reconstruction.

Three-Dimensional Lidar Localization and Mapping with Loop-Closure Detection Based on Dense Depth Information

This paper presents a novel lidar SLAM system for localizing a mobile robot to build a map of the environment. To identify the unknown transform matrix, we design a new scan-matching approach, in which a point cloud segmentation algorithm is additionally integrated. Different from the traditional normal distribution transform algorithm for point cloud registration, our newly proposed one additionally incorporates a ground point remover and a point cloud segmentation method. By employing the point cloud segmentation algorithm to divide the point cloud space into different cells, the newly proposed algorithm can guarantee the continuity and convergence of the cost function. To tackle the recognition difficulties that the camera-based loop-closure detection heavily depends on the environment’s appearance, a depth-completion algorithm is introduced to fuse sensor data to ensure the robustness of the algorithm. Moreover, the bags of binary words (DBoW) are adopted to improve the image-matching quality. Finally, experimental results are presented to illustrate the effectiveness of the proposed system.

Estimating Effective Leaf Area Index of Winter Wheat Based on UAV Point Cloud Data

Leaf area index (LAI) is a widely used plant biophysical parameter required for modelling plant photosynthesis and crop yield estimation. UAV remote sensing plays an increasingly important role in providing the data source needed for LAI extraction. This study proposed a UAV-derived 3-D point cloud-based method to automatically calculate crop-effective LAI (LAIe). In this method, the 3-D winter wheat point cloud data filtered out of bare ground points was projected onto a hemisphere, and then the gap fraction was calculated through the hemispherical image obtained by projecting the sphere onto a plane. A single-angle inversion method and a multi-angle inversion method were used, respectively, to calculate the LAIe through the gap fraction. The results show a good linear correlation between the calculated LAIe and the field LAIe measured by the digital hemispherical photography method. In particular, the multi-angle inversion method of stereographic projection achieved the highest accuracy, with an R2 of 0.63. The method presented in this paper performs well in LAIe estimation of the main leaf development stages of the winter wheat growth cycle. It offers an effective means for mapping crop LAIe without the need for reference data, which saves time and cost.

Adaptive Active Voltage-Type Arc Suppression Strategy Considering the Influence of Line Parameters in Active Distribution Network

The reliable extinguishment of a single phase-to-ground (SPG) arc is the key issue for safe and reliable operation in the distribution network. However, Since the existing arc suppression methods are normally affected by the line parameters, the residual current (RC) at the grounding point still exists, then the grounding arc is hard to be extinguished reliably. Thus, an adaptive active voltage-type arc suppression (AAVAS) strategy that can reliably inhibit the RC at the grounding point is proposed in this paper. Firstly, the mechanism of the traditional voltage-type arc extinguishing method affected by the line parameters is proved, and the variation rules are given for the RC under different fault distances and grounding resistance. Secondly, the variation characteristics are analyzed for the faulty line current before and after arc extinguishing. On this basis, it is proposed to add the residual voltage (RV) compensation ring to the traditional voltage-type control strategy, which can effectively offset the influence of voltage drop on faulty-phase line impedance. Then, the Bode diagram analysis shows that the presented strategy performs well regardless of ground-fault conditions. Finally, the feasibility and effectiveness of the proposed AAVAS strategy are verified by the simulation and experiment results.

Research on Power Line Extraction and Modeling Technology Based on Laser Point Cloud

For the operation and maintenance management of Power Lines, this paper uses laser scanners to collect data from the research lines, and proposes a method combining random sampling consistency with Euclidean clustering to extract the traverse point cloud data. Firstly, the idea of random sampling consistency is used to eliminate a large number of ground points unrelated to target points. Secondly, Euclidean clustering is used to extract traverse and tower points, and then the tower and traverse are divided by combining tower coordinates and threshold. Finally, the polynomial model is used to construct a fitting traverse. The results show that this method can effectively extract the traverse points and accurately fit the traverse model, and it has certain applicability.

Towards an automated acquisition and parametrization of debris‐flow prone torrent channel properties based on photogrammetric‐derived uncrewed aerial vehicle data

AbstractDebris flows are a hazard in mountainous regions. Cost‐effective, long‐term studies of debris‐flow torrents, however, are rare, leading to uncertainties in hazard assessment and hazard prevention. Here, we address the question of whether cost‐effective remote sensing techniques can be applied for assessment of mountain torrents and possibly further gather accurate, long‐term information on the evolution of the catchment. Torrents prone to debris flows are often devoid of vegetation in the near channel area and hence can be well captured with photogrammetrically derived methods using uncrewed aerial vehicle (UAV) surveys. The possibility of automatically extracting specific torrent parameters from high‐resolution terrain models, such as cross‐section area or gradient, is investigated. The presented methodology yields continuous and automatically derived geometrical parameters such as torrent bed width, inclination and cross‐section area, which is a major advantage compared with point‐based, often dangerous field surveys. Their cross‐validation with field measurements shows strong agreement. Those parameters are accurate along sharply incised sections with strong limitations along sections with steep adjacent slopes and/or dense vegetation. The information along the torrent allows fast identification of key sections and weak spots which can be precisely evaluated in the field. The study highlights that proper classification of real ground points poses the key challenge. We show that photogrammetric routines to derive a high‐resolution digital terrain model (DTM) are limited in the case of dense vegetation coverage. In such cases, LiDAR surveys have clear advantages even though they are also limited by very dense vegetation. We find that UAV data can be used for an objective method of estimating debris‐flow torrent geometric properties. And the introduced approaches therefore build a stepping stone towards a more comprehensive, reproducible and objective assessment of torrent processes and predispositions. However, ground‐referencing fieldwork remains essential, and further research on remote sensing supported hazard assessment of debris‐flow‐prone torrents is indispensable.

Designed on 0.18 μm CMOS Process Small Size Broadband Millimeter Wave Chip Antenna

This paper proposes a small-size broadband triangular monopole chip antenna for millimeter wave band applications. Process using 0.18 μm CMOS process and antenna design using Met-al_6. Triangular patch design and feed line length analysis to achieve a better reflection coefficient and also dig three circular slots at the grounding point to achieve better impedance matching. The operating frequency of the chip antenna is 62–100 GHz below the reflection coefficient −10 dB standard, with a fractional bandwidth of 54%. The maximum gain is −0.4 dBi at 64 GHz. The efficiency is 40.9%. The overall chip size is 1.2 × 1.2 (mm2). After measurement and verification, the proposed antenna reflection coefficient is similar to the simulation trend and has better resonance. The chip antenna frequency range proposed in this article covers the 5G NR FR2 frequency band. The proposed chip antenna can be applied in related fields such as the Internet of Things, Industry 4.0, and biomedical electronics.

Numerical simulation of the development process of lightning with two grounding points

The lightning with two grounding points (TGP) is a special and more destructive lightning. Due to the lack of observational data, many physical problems of TGP lightning are still unclear. This paper attempts to establish a theoretical model to simulate the development process of TGP lightning. The simulation results show that the average horizontal distance between two grounding points is about half the average height of the branch point. Both branches move toward the ground at almost the same speed. The formation of TGP lightning seems to be independent of the height of the branch point. The results of our simulation are in good agreement with those found in nature for TGP lightning. This work contributes to a more comprehensive understanding of the TGP lightning. It also can provide reference for the modeling of lightning with multiple grounding points.

Road-Side Individual Tree Segmentation from Urban MLS Point Clouds Using Metric Learning

As one of the most important components of urban space, an outdated inventory of road-side trees may misguide managers in the assessment and upgrade of urban environments, potentially affecting urban road quality. Therefore, automatic and accurate instance segmentation of road-side trees from urban point clouds is an important task in urban ecology research. However, previous works show under- or over-segmentation effects for road-side trees due to overlapping, irregular shapes and incompleteness. In this paper, a deep learning framework that combines semantic and instance segmentation is proposed to extract single road-side trees from vehicle-mounted mobile laser scanning (MLS) point clouds. In the semantic segmentation stage, the ground points are filtered to reduce the processing time. Subsequently, a graph-based semantic segmentation network is developed to segment road-side tree points from the raw MLS point clouds. For the individual tree segmentation stage, a novel joint instance and semantic segmentation network is adopted to detect instance-level roadside trees. Two complex Chinese urban point cloud scenes are used to evaluate the individual urban tree segmentation performance of the proposed method. The proposed method accurately extract approximately 90% of the road-side trees and achieve better segmentation results than existing published methods in both two urban MLS point clouds. Living Vegetation Volume (LVV) calculation can benefit from individual tree segmentation. The proposed method provides a promising solution for ecological construction based on the LVV calculation of urban roads.

Mapping stone walls in Northeastern USA using deep learning and LiDAR data

ABSTRACT Stone walls are widespread and iconic landforms found throughout forested terrain in the Northeastern USA that were built during the 17th to early 20th centuries to delineate property boundaries and the edges of agricultural fields and pastures. As linear, or broadly curved, features that are typically >0.5 m high, 1–2 m wide, and >4–8 m long, stone walls are highly visible in LiDAR data, and mapping them is of broad interest to the cultural heritage sector as well as to researchers specifically focused on historic landscape reconstruction. However, existing mapping attempts have commonly relied on field surveys and manual digitization, which is time-consuming, especially when trying to complete mapping at broader scales. In response to this limitation, this study: (1) presents a novel framework to automate stone wall mapping using Deep Convolutional Neural Networks (DCNN) models (U-Net and ResUnet) and high-resolution airborne LiDAR, (2) evaluates model performance in two test sites against field verified stone walls, (3) investigates the factors that can influence model performance in terms of the quality of LiDAR data (e.g. ground point spacing), and (4) suggests post-processing for town-level mapping of stone walls (~120 km2). Both models performed well with respect to the Matthews Correlation Coefficient (MCC) score. U-Net scenario 3 achieved an MCC score of 0.87 at test site 1, while ResUnet scenario 3 (S3) had an MCC score of 0.80 at test site 2. In town-level test site 3, ResUnet S3 achieved the best F1 score of 82% after post-processing. This study demonstrates the potential of automated mapping of anthropogenic features using our models.

P‐2.21: Research on multi‐lidar detection technology for autonomous driving

Nowadays autonomous driving has become a hot topic, many people pay attention to the field. Target detection through sensors is the basis of autonomous driving and plays an important role in high-level autonomous driving. The effective environmental information obtained by using a single sensor is less, and the visual sensor is easily affected by the weather and the algorithm is complex, so there are problems in real-time detection and accuracy. Multi-lidar detection is also a popular research in this field and has important research value and application prospect.This paper mainly describes the key steps of multi-lidar detection and proposes a fully automatic point cloud registration scheme. The fully automatic calibration method does not require manual initialization and does not rely on additional sensors at all, which can play a good role in multi-lidar detection. After that, the fused point cloud data is filtered to remove interference points and reduce the number of point clouds. In view of the under-segmentation of ground point cloud, the point cloud is mapped to the raster graph, and the ground point and non-ground point are separated by Random sample consensus algorithm and least square method. Then, distance-based clustering is used for obstacle detection on non-ground points.