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.

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.

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.

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.

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.

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。

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).

基于无人机载LiDAR点云数据的电力线提取方法

机载激光雷达(LiDAR)点云电力线提取过程中存在杆塔形状复杂、噪声影响大等问题,导致电力线点云提取精度低,本文提出一种基于点云分块处理、格网划分的曲面拟合滤波、自适应密度聚类算法的电力线点云提取与重建方法。首先,根据电力线走向,对整体点云进行分块处理;其次,在曲面拟合算法的基础上,引入格网划分思想,提出一种改进曲面拟合滤波算法并进行点云滤波;最后,通过给出自适应密度聚类解决方案精确提取电力线点云。借助点云库(PCL)、libLAS库与Visual Studio 2017 C++开发环境实现本文算法,基于实测点云数据对本文方法进行测试与精度评定。结果表明:电力线提取精确率为97.82%、召回率为99.76%、F1值为98.78%,一次便可实现电力线的成功提取,在保证提取精度的同时提升了提取效率,本文研究能够为电力线智能巡检提供良好的工程应用价值。

无人机LiDAR免控制测量技术在1∶10000 DEM更新中的应用

在安徽省1∶10000基础地理信息数据更新工程DEM局部更新工作中,针对外业中控制点布设工作量大、难度高的特点,研究无人机LiDAR测量技术生产DEM的免控制技术方法,利用激光点云分类结果中提取的可靠地面点,建立高程拟合模型,拟合转换点云高程,生产DEM。并与传统通过实测控制点进行点云高程拟合生产的DEM成果精度比较。研究结果表明:利用免控制技术方法生产的DEM精度可以满足安徽省1∶10000 DEM更新要求。

基于改进四叉树的LiDAR点云数据组织研究

分析了点云数据处理中常用数据组织方法,并指出方法的性能判定指标。对常用的构建四叉树方法进行了改进以提高建立四叉树索引的速度,分析及改进索引算法改进以增进数据筛选的速度,最后通过实验证明了该方法的有效性和可靠性。

多级移动曲面拟合LIDAR数据滤波算法

为提高城市区LIDAR数据滤波精度,提出了一种多级移动曲面拟合滤波方法。建立区块网格搜寻及索引机制完成对离散LIDAR点云的标示;通过建立二次多项式完成参考曲面的拟合,不同窗口大小获得不同层次的拟合曲面;设置自适应阈值,完成地面点与非地面点的判断。精度评价结果表明,该滤波算法误差在1m以内,能够满足实际应用的需求。

无需阈值支持的机载LiDAR点云数据滤波方法

点云数据滤波仍旧是现阶段机载LiDAR数据后处理的首要步骤,但其发展尚未完全成熟。在回顾和总结已有滤波算法的基础上,将统计学中偏度与峰度的概念引入到算法中,提出了一种新的基于偏度平衡的地面点与非地面点非监督分类方法,利用统计矩原理从LiDAR点云数据生成的DSM中有效地提取DTM。该方法区别传统算法的最大的优势在于无需参数或者阈值支持,并且相对于LiDAR点云数据的格式和分辨率是独立的。实验结果证明,该方法切实可行,具有较强的适应性,并且能够较好地满足精度要求。

机载LIDAR点云数据估测单株木生物量

使用黑河流域遥感-地面观测同步试验获取的机载激光雷达(LIDAR)点云数据估测了典型树种的单株木生物量。首先由点云数据生成冠层高度模型(CHM),然后采用优化的单株木树冠特征识别算法估测相关的结构参数,最后通过回归分析建立估测参数(树高、冠幅)与实测参数(树高、冠幅、胸径)之间的最优回归方程,并与现有的单株木生物量实测相关生长方程联立,得到单株木生物量估测相关生长方程。结果表明,由LIDAR点云数据得到的单株木估测参数与实测参数显著相关,可以估测单株木生物量(R^2为0.729)。

结合影像和LiDAR点云数据的水体轮廓线提取方法

从遥感影像中提取水体的传统方法在阴影处和水体浑浊处存在一定局限;机载激光雷达(LiDAR)获取点云数据时水体受阴影和浑浊的影响小,因此使用点云数据提取水体比使用影像数据更加稳健;但点云数据的分辨率低,所提取的轮廓线精度不高。为了提高水体边缘轮廓线的精确度,提出一种高分辨率影像与点云数据相结合的水体轮廓线提取方法。实验结果表明,该方法所提取的水体轮廓线定位精确、细节完好,水体提取准确率达到98.6%。

适用于林区机载LiDAR点云的多分辨率层次插值滤波方法

针对现有机载激光雷达点云滤波算法在林区适用性不强的问题,提出一种基于多分辨率层次插值的林区LiDAR滤波方法。该方法首先借助形态学迭代开运算和稳健z-score方法获取大量地面种子点;然后从低层到高层滤波过程中,通过薄板样条函数构造地面参考面,并借助自适应坡度阈值选择地面点;最后将分类出的地面点更新地面参考面,层层迭代直至滤波结果收敛。以ISPRS提供的6组山区基准数据为研究对象,将新方法滤波结果与近5年提出的10种滤波算法比较表明:新方法滤波结果精度最高,平均总误差和Kappa系数分别为1.89%和87.88%。在实例分析中,以6个不同林区点云数据为研究对象,将新方法与形态滤波算法(MF)和渐近不规则三角网加密滤波算法(PTD)比较表明:新方法平均总误差为6.82%,而MF和PTD平均总误差分别为9.21%和8.49%;且前者获取的DEM精度优于后两种方法。

基于等高线形状分析的LIDAR建筑物提取

基于等高线形状分析,提出了一种新的LIDAR(机载激光雷达)建筑物提取方法.首先利用LIDAR数据生成DSM(数字地面模型)等高线,然后根据等高线形状特征参数,从DSM等高线中提取由建筑物点云形成的等高线(建筑物等高线).最后,根据拓扑关系简化建筑物等高线,再利用建筑物轮廓线相互垂直或平行的特点,对建筑物等高线进行规则化,提取建筑物.实验结果表明,即使在地形起伏的情况下,该方法仍可提取出形状复杂的建筑物,准确率达80%以上.

LIDAR点云中多层屋顶轮廓线提取方法研究

在分析LIDAR数据提取建筑物轮廓线现状的基础上,针对多层、非规则屋顶轮廓线提取的难点,提出一种直接基于离散点云的屋顶轮廓线提取方法,该方法主要包括屋顶点的识别,初始轮廓线的提取以及轮廓线的规则化等步骤。最后采用实地数据进行验证,结果表明该方法具有一定的应用前景。

基于机载LiDAR和多光谱图像的建筑物震害自动识别方法

地震破坏的建筑物在遥感影像上和空间上表现出的特征各异,致使遥感定量化估计其破坏程度较困难。本文介绍了基于LiDAR和多光谱影像相结合的多源遥感影像进行倒塌建筑物的面向对象识别的方法、分析处理步骤和特征参数选择,并以2010年1月12日海地地震后的太子港局部LiDAR数据和高分辨率卫星影像为例,提取了倒塌和未倒塌建筑物,经与高分影像目视解译结果比较,面向对象分类结果具有较高的分类精度。

基于LIDAR数据的城市数字表面模型生成技术

激光雷达(LIDAR)数据是一种新型数据源,它产生的是高密度点云数据。为了更加方便地应用这些数据,首先要生成数字表面模型(DSM)。采用传统的方法生成城市DSM,对城市区域复杂性的考虑不足,也没有对数据存在的缺值情况进行相应处理,故无法生成高质量的城市DSM。该文阐述了一种新颖的生成高质量城市DSM的方法,兼顾城市的复杂性和LIDAR传感器本身的特点。试验证明,该方法生成的DSM与传统方法相比,具备更好的效果。