Development of vehicle-recognition method on water surfaces using LiDAR data:SPD^(2)(spherically stratified point projection with diameter and distance)

Swarm robot systems are an important application of autonomous unmanned surface vehicles on water surfaces.For monitoring natural environments and conducting security activities within a certain range using a surface vehicle,the swarm robot system is more efficient than the operation of a single object as the former can reduce cost and save time.It is necessary to detect adjacent surface obstacles robustly to operate a cluster of unmanned surface vehicles.For this purpose,a LiDAR(light detection and ranging)sensor is used as it can simultaneously obtain 3D information for all directions,relatively robustly and accurately,irrespective of the surrounding environmental conditions.Although the GPS(global-positioning-system)error range exists,obtaining measurements of the surface-vessel position can still ensure stability during platoon maneuvering.In this study,a three-layer convolutional neural network is applied to classify types of surface vehicles.The aim of this approach is to redefine the sparse 3D point cloud data as 2D image data with a connotative meaning and subsequently utilize this transformed data for object classification purposes.Hence,we have proposed a descriptor that converts the 3D point cloud data into 2D image data.To use this descriptor effectively,it is necessary to perform a clustering operation that separates the point clouds for each object.We developed voxel-based clustering for the point cloud clustering.Furthermore,using the descriptor,3D point cloud data can be converted into a 2D feature image,and the converted 2D image is provided as an input value to the network.We intend to verify the validity of the proposed 3D point cloud feature descriptor by using experimental data in the simulator.Furthermore,we explore the feasibility of real-time object classification within this framework.

Rail-Pillar Net:A 3D Detection Network for Railway Foreign Object Based on LiDAR

Aiming at the limitations of the existing railway foreign object detection methods based on two-dimensional(2D)images,such as short detection distance,strong influence of environment and lack of distance information,we propose Rail-PillarNet,a three-dimensional(3D)LIDAR(Light Detection and Ranging)railway foreign object detection method based on the improvement of PointPillars.Firstly,the parallel attention pillar encoder(PAPE)is designed to fully extract the features of the pillars and alleviate the problem of local fine-grained information loss in PointPillars pillars encoder.Secondly,a fine backbone network is designed to improve the feature extraction capability of the network by combining the coding characteristics of LIDAR point cloud feature and residual structure.Finally,the initial weight parameters of the model were optimised by the transfer learning training method to further improve accuracy.The experimental results on the OSDaR23 dataset show that the average accuracy of Rail-PillarNet reaches 58.51%,which is higher than most mainstream models,and the number of parameters is 5.49 M.Compared with PointPillars,the accuracy of each target is improved by 10.94%,3.53%,16.96%and 19.90%,respectively,and the number of parameters only increases by 0.64M,which achieves a balance between the number of parameters and accuracy.

Predicting the provisioning potential of forest ecosystem services using airborne laser scanning data and forest resource maps

Background: Remote sensing-based mapping of forest Ecosystem Service(ES) indicators has become increasingly popular. The resulting maps may enable to spatially assess the provisioning potential of ESs and prioritize the land use in subsequent decision analyses. However, the mapping is often based on readily available data, such as land cover maps and other publicly available databases, and ignoring the related uncertainties.Methods: This study tested the potential to improve the robustness of the decisions by means of local model fitting and uncertainty analysis. The quality of forest land use prioritization was evaluated under two different decision support models: either using the developed models deterministically or in corporation with the uncertainties of the models.Results: Prediction models based on Airborne Laser Scanning(ALS) data explained the variation in proxies of the suitability of forest plots for maintaining biodiversity, producing timber, storing carbon, or providing recreational uses(berry picking and visual amenity) with RMSEs of 15%–30%, depending on the ES. The RMSEs of the ALS-based predictions were 47%–97%of those derived from forest resource maps with a similar resolution. Due to applying a similar field calibration step on both of the data sources, the difference can be attributed to the better ability of ALS to explain the variation in the ES proxies.Conclusions: Despite the different accuracies, proxy values predicted by both the data sources could be used for a pixel-based prioritization of land use at a resolution of 250 m~2, i.e., in a considerably more detailed scale than required by current operational forest management. The uncertainty analysis indicated that maps of the ES provisioning potential should be prepared separately based on expected and extreme outcomes of the ES proxy models to fully describe the production possibilities of the landscape under the uncertainties in the models.

林区机载LiDAR点云的多分辨率层次布料模拟滤波

针对现有机载LiDAR(light detection and ranging)点云滤波方法在地形起伏剧烈的林区适用性不足的问题,提出一种多分辨率层次布料模拟滤波方法。首先,通过多尺度形态学开运算选择大量种子地面点;然后,基于种子地面点,使用布料模拟法由低至高逐层构建参考地形,以快速获取高分辨率参考地形;最后,基于点至参考地形的高差区分地面点和非地面点。利用国际摄影测量和遥感学会提供的数据集和参考方法,评估该方法性能。利用在中国、美国多个代表性林区的点云数据,评估该方法的可推广性。结果表明,该方法的Kappa系数和运行时间是83.72%和34.11 s,精度和效率较经典布料模拟滤波方法提高10.49%和52.17%。相比8种参考方法,该方法能够获得更高精度,并且具有稳定的可推广性。

Multi-Scale Feature Extraction for Joint Classification of Hyperspectral and LiDAR Data

With the development of sensors,the application of multi-source remote sensing data has been widely concerned.Since hyperspectral image(HSI)contains rich spectral information while light detection and ranging(LiDAR)data contains elevation information,joint use of them for ground object classification can yield positive results,especially by building deep networks.Fortu-nately,multi-scale deep networks allow to expand the receptive fields of convolution without causing the computational and training problems associated with simply adding more network layers.In this work,a multi-scale feature fusion network is proposed for the joint classification of HSI and LiDAR data.First,we design a multi-scale spatial feature extraction module with cross-channel connections,by which spatial information of HSI data and elevation information of LiDAR data are extracted and fused.In addition,a multi-scale spectral feature extraction module is employed to extract the multi-scale spectral features of HSI data.Finally,joint multi-scale features are obtained by weighting and concatenation operations and then fed into the classifier.To verify the effective-ness of the proposed network,experiments are carried out on the MUUFL Gulfport and Trento datasets.The experimental results demonstrate that the classification performance of the proposed method is superior to that of other state-of-the-art methods.

Laser diode drive method with narrow-width and high-peak current for multi-line LIDAR

Light detection and ranging (LIDAR) based on time of flight (TOF) method is widely used in many fields related to distance measurement. LIDAR generally uses laser diode (LD) to emit the pulsed laser with high peak power and short duration to ensure a large distance measurement range and eye safety. To achieve this goal, we propose a pulsed LD drive method producing the drive current with high peak and narrow pulse width. We analyze the key issues and related theories of the drive current generation based on this method and design an LD driver. A model of drive current generation is established and the influence of operating frequency on drive current is discussed. The LD driver is simulated by software and verified by experiments. The working frequency of the driver changes from 20 kHz to 100 kHz and the charging voltage is set at 130 V. The current produced by this driver has a duration of 8.8 ns and a peak of about 35 A, and the peak output optical power of the LD exceeds 75 W.

Effects of laser beam divergence angle on airborne LIDAR positioning errors

The influence of laser beam divergence angle on the positioning accuracy of scanning airborne light detection and ranging （LIDAR） is analyzed and simulated. Based on the data process and positioning principle of airborne LIDAR, the errors from pulse broadening induced by laser beam di vergence angle are modeled and qualitatively analyzed for different terrain surfaces. Simulated results of positioning errors and suggestions to reduce them are given for the flat surface, the downhill of slope surface, and the uphill surface.

Three-channel CMOS transimpedance amplifier for LiDAR sensor receiver

For time-of-flight(TOF)light detection and ranging(LiDAR),a three-channel high-performance transimpedance amplifier(TIA)with high immunity to input load capacitance is presented.A regulated cascade(RGC)as the input stage is at the core of the complementary metal oxide semiconductor(CMOS)circuit chip,giving it more immunity to input photodiode detectors.A simple smart output interface acting as a feedback structure,which is rarely found in other designs,reduces the chip size and power consumption simultaneously.The circuit is designed using a 0.5μm CMOS process technology to achieve low cost.The device delivers a 33.87 dB?transimpedance gain at 350 MHz.With a higher input load capacitance,it shows a-3 dB bandwidth of 461 MHz,indicating a better detector tolerance at the front end of the system.Under a 3.3 V supply voltage,the device consumes 5.2 mW,and the total chip area with three channels is 402.8×597.0μm2(including the test pads).

Mapping tree canopies in urban environments using airborne laser scanning (ALS):a Vancouver case study

Background: The distribution of forest vegetation within urban environments is critically important as it influences urban environmental conditions and the energy exchange through the absorption of solar radiation and modulation of evapotranspiration. It also plays an important role filtering urban water systems and reducing storm water runoff.Methods: We investigate the capacity of ALS data to individually detect, map and characterize large(taller than15 m) trees within the City of Vancouver. Large trees are critical for the function and character of Vancouver’s urban forest. We used an object-based approach for individual tree detection and segmentation to determine tree locations(position of the stem), to delineate the shape of the crowns and to categorize the latter either as coniferous or deciduous.Results: Results indicate a detection rate of 76.6% for trees > 15 m with a positioning error of 2.11 m(stem location). Extracted tree heights possessed a RMSE of 2.60 m and a bias of-1.87 m, whereas crown diameter was derived with a RMSE of 3.85 m and a bias of-2.06 m. Missed trees are principally a result of undetected treetops occurring in dense, overlapping canopies with more accurate detection and delineation of trees in open areas.Conclusion: By identifying key structural trees across Vancouver’s urban forests, we can better understand their role in providing ecosystem goods and services for city residents.

On the potential to predetermine dominant tree species based on sparse-density airborne laser scanning data for improving subsequent predictions of species-specific timber volumes

Background： Tree species recognition is the main bottleneck in remote sensing based inventories aiming to produce an input for species-specific growth and yield models. We hypothesized that a stratification of the target data according to the dominant species could improve the subsequent predictions of species-specific attributes in particular in study areas strongly dominated by certain species. Methods： We tested this hypothesis and an operational potential to improve the predictions of timber volumes, stratified to Scots pine, Norway spruce and deciduous trees, in a conifer forest dominated by the pine species. We derived predictor features from airborne laser scanning （ALS） data and used Most Similar Neighbor （MSN） and Seemingly Unrelated Regression （SUR） as examples of non-parametric and parametric prediction methods, respectively Results： The relationships between the ALS features and the volumes of the aforementioned species were considerably different depending on the dominant species. Incorporating the observed dominant species inthe predictions improved the root mean squared errors by 13.3-16.4 % and 12.6-28.9 % based on MSN and SUR, respectively, depending on the species. Predicting the dominant species based on a linear discriminant analysis had an overall accuracy of only 76 % at best, which degraded the accuracies of the predicted volumes. Consequently, the predictions that did not consider the dominant species were more accurate than those refined with the predicted species. The MSN method gave slightly better results than models fitted with SUR. Conclusions： According to our results, incorporating information on the dominant species has a clear potential to improve the subsequent predictions of species-specific forest attributes. Determining the dominant species based solely on ALS data is deemed challenging, but important in particular in areas where the species composition is otherwise seemingly homogeneous except being dominated by certain species.

Segments-based progressive TIN densification filter for DTM generation from airborne LIDAR data

Airborne light detection and ranging( LIDAR) has revolutionized conventional methods for digital terrain models( DTMs) acquisition. Ground filtering for airborne LIDAR is one of the core steps taken to obtain a high quality DTM. This paper presents a segments-based progressive TIN( triangulated irregular network) densification( SPTD) filter that can automatically separate ground points from non-ground points. The SPTD method is composed of two key steps: point cloud segmentation and clustering by iterative judgement. The clustering method uses the dual distance to obtain a set of seed points as a coarse spatial clustering process. Then the rest of the valid point clouds are classified iteratively. Finally,the datasets provided by ISPRS are utilized to test the filtering performance.In comparison with the commercial software Terra Solid,the experimental results show that the SPTD method in this paper can avoid single threshold restrictions. The expected accuracy of ground point determination is capable of producing reliable DTMs in the discontinuous areas.

不同森林覆盖类型对LiDAR生成DEM的精度影响分析

运用机载激光雷达(LiDAR)获取的高精度数字高程模型(DEM)是目前地学分析应用满足精细化要求的基础数据。目前对森林覆盖下DEM产品的精度影响分析依然有所欠缺。以实时动态载波相位差分技术(RTK)结合全站仪实测的高程碎部点与机载LiDAR数据生成的高精度DEM产品数值做差,进行单因素方差分析(ANOVA)检验以及独立样本T-检验。经多重比较分析(LSD)可知,以马尾松为代表的针叶林和以桉树、茶树、黄连木、荔枝、阔叶混合林为代表的阔叶林之间差值相差较大,说明针叶林和阔叶林两种不同森林覆盖类型的绝对精度存在差异。但Robust检验结果显示阔叶林地表DEM的精度均值相等性为1,说明阔叶林(以桉树、茶树、黄连木、荔枝、阔叶混合林林地为代表)地表DEM精度不受树种的影响。综上,阔叶林与针叶林的地表DEM精度差异显著,说明阔叶林和针叶林对机载LiDAR生成森林区域地表DEM存在一定的影响。

高精度LiDAR技术在活动断层断错地貌研究中的应用——以沂沭断裂带莒县至郯城段为例

郯庐断裂带是我国东部地区最为重要的大型活动断裂之一,具有较强的发震能力,断裂带沿线多处穿过人类活动及聚集的城市。为了研究郯庐断裂带的几何展布及活动性特征,选取郯庐断裂带的沂沭断裂莒县至郯城范围为研究对象,考虑东部地区植被覆盖,采用机载激光雷达和地基激光雷达手段采集了断裂带沿线左山(一步涧)段、钟华山段、岌山段、马陵山段以及蒋家岭等地高精度地形数据,对断裂的滑动分布特征进行分析。通过对测量得到的点云数据进行点云匹配、镶嵌、植物滤除以及不规则三角网建模处理,得到了0.1 m空间分辨率的数字高程模型(Digital Elevation Model,DEM),实现了对断裂微地貌形态的高清晰度三维再现,提取了断裂断错冲沟右旋水平位错量和断层陡坎垂直位错量,结合地质填图和探槽揭露的古地震事件进行验证和对比,对郯庐断裂带莒县至郯城段的断层活动特征进行了定量分析。结果表明,沿断裂带左山(一步涧)段、钟华山段、岌山段和马陵山段的水平和垂直位错量具有分级特征,晚第四纪以来可能发生过多期(3~5次)活动和多次古地震事件。研究成果为进一步研究郯庐断裂运动学和几何学提供了重要依据,同时也说明地基激光雷达和机载激光雷达技术在活动断层研究中有着广泛的应用前景。

3-D Reconstruction and Visualization of Laser-Scanned Trees by Weighted Locally Optimal Projection and Accurate Modeling Method

This paper presents a method to reconstruct 3-D models of trees from terrestrial laser scan(TLS)point clouds.This method uses the weighted locally optimal projection(WLOP)and the AdTree method to reconstruct detailed 3-D tree models.To improve its representation accuracy,the WLOP algorithm is introduced to consolidate the point cloud.Its reconstruction accuracy is tested using a dataset of ten trees,and the one-sided Hausdorff distances between the input point clouds and the resulting 3-D models are measured.The experimental results show that the optimal projection modeling method has an average one-sided Hausdorff distance(mean)lower by 30.74%and 6.43%compared with AdTree and AdQSM methods,respectively.Furthermore,it has an average one-sided Hausdorff distance(RMS)lower by 29.95%and 12.28%compared with AdTree and AdQSM methods.Results show that the 3-D model generated fits closely to the input point cloud data and ensures a high geometrical accuracy.

基于机载LiDAR点云的建筑物多细节层次建模方法

机载激光雷达点云(LiDAR)重建大范围建筑物模型一直是实景三维建模的热点问题,由于机载激光点云稀疏、建筑物立面数据缺失,给模型自动化重建带来了极大的挑战。为了解决该问题,本论文研究基于图优化理论重建建筑物多细节层次(LOD)模型。首先,对原始点云进行自动化滤波,分离地面点和非地面点,对地面点构建数字高程模型(DEM),对非地面点半自动提取单体化建筑物点云。然后,基于滚球法(Alpha shape)提取建筑物边界,利用通用图优化方法(G2O)对误差线进行全局一致性改正,获得规则化的二维边框。并基于建筑物屋顶三维高程及DEM高程值重建建筑物LOD1模型。其次,根据点云的高程差异生成高程栅格图,从高程栅格图提取建筑物轮廓线,对轮廓线进行简化、规则化、聚类,并将规则后的边界线拉伸获取建筑物立面结构,弥补建筑物立面数据缺失对建模的影响。最后,将屋顶平面相交、建筑物立面裁剪,对候选平面进行二元图割全局优化,选择最能表达建筑物结构的平面,以此重建建筑物LOD2模型。本论文选择北京市2017年机载激光点云进行实验,结果表明,本文提出的方法可以稳健地重建建筑物多细节层次模型,LOD2模型距离偏差为0.22 m,LOD1模型距离偏差为0.71 m,整体模型精确度高;针对1147个建筑物,LOD2和LOD1模型重建时间分别为1290s和1097s,具有较高的自动化程度和建模效率。

乌鲁木齐机场一次冷锋型低空风切变过程的LiDAR分析

为了研究低空风切变风场结构,针对乌鲁木齐机场2021-11-26发生的风切变不安全事件,采用FC-Ⅲ型激光测风雷达产品数据,配合美国国家环境预报中心再分析资料和常规气象观测资料进行分析和验证,取得了风切变演变过程的数据。结果表明,该次风切变过程发生在特定的地形风作用下,冷锋前小尺度冷空气造成显著的风向风速变化,东南风急流底部与西北风风带形成倾斜向上的垂直切变区,并引发冷锋型低空风切变;风切变发生前1 h,乌鲁木齐机场周边出现了风场转换;平面位置显示模式比航空器报告提前10 min监测到风切变,为东南大风风速骤减区,且风切变区随冷空气渗透西移;冷空气渗透过程东南大风层变薄西撤;07^(#)跑道附近,正侧风迅速减小且进近过程中伴有风向的大角度转变;冷空气由25^(#)跑道向07^(#)跑道楔形渗透,渗透过程发生在08:30~10:25期间;激光雷达捕捉到该次低空小尺度冷空气活动,分析出冷空气由东北侧进入呈后倾状态的演变过程和结构,并触发了中度风切变预警。这一结果对提高气象服务保障能力是有帮助的。

基于机载LiDAR点云的电力线提取与三维重建

为了解决地形复杂、点云密度不均匀的输电线机载激光雷达(LiDAR)点云电力线提取精度低的问题,本文根据电力线点的空间分布特征设计与实现了一套电力线提取与三维重建方法。首先,使用改进曲面拟合滤波算法与形态学开运算实现地面点、低矮植被点等的滤除;其次,以滤波处理得到点云数据为数据源,利用电力线点维度特征实现电力线点粗提取并利用密度聚类算法进行单根电力线精提取;最后,基于单根电力线提取结果进行电力线三维重建。为了对本文提出电力线提取与重建方法进行检验,使用宁波市某高压交流输变电工程中部分实测机载LiDAR点云数据进行实验,结果表明,本文方法提取28根电力线结果误差率均在0.04%以内,验证了本文方法的可靠性与实用性。

机载LiDAR河流高程拟合算法

使用机载激光雷达(LiDAR)进行数字高程模型(DEM)制作过程中,河流数据缺失,需进行人工编辑,目前处理流程中难以既保证数据精度又保证整体平整美观,本文提出机载LiDAR河流高程拟合方案,通过半自动河流边线提取、河流中心线提取以及中心线高程拟合一系列技术流程,不仅解决当前复杂的人工编辑问题,还提升了拟合精度,文中详细地阐述河流高程拟合关键算法,并基于Micro station V8开发出应用实例,为机载LiDAR河流高程拟合提供新思路。

面向机载LiDAR点云数据的双线河水体DEM构建方法

机载激光雷达(LiDAR)系统可以快速获取高精度的地面点云数据,可以快速生成数字高程模型(DEM)。但是基于原始水体点云数据直接构建DEM效果较差,针对这个问题,论文提出了一种双线河水体点云填充方法,对缺失的河道内点云数据进行填充,并设计开发了河流优化填充模块,实现了双线河水体具有上下游特征的DEM构建效果。

倾斜摄影和LiDAR技术融合在大比例尺地形图测绘中的应用

地形图传统测量方法费时费力。本文融合倾斜摄影与激光探测及测距系统技术,以抚州市比亚迪厂区地形图测绘项目为例,非房屋区域采用机载激光雷达航测技术,房屋区域采用倾斜摄影测量航测技术成图。最后通过外业采集检查点与内业测图成果进行精度对比分析。结果表明:该技术测绘成果平面与高程均能满足大比例尺测图的精度要求,而且能节约地形图制作时间及成本。