Estimating wood quality attributes from dense airborne LiDAR point clouds

Mapping individual tree quality parameters from high-density LiDAR point clouds is an important step towards improved forest inventories.We present a novel machine learning-based workflow that uses individual tree point clouds from drone laser scanning to predict wood quality indicators in standing trees.Unlike object reconstruction methods,our approach is based on simple metrics computed on vertical slices that summarize information on point distances,angles,and geometric attributes of the space between and around the points.Our models use these slice metrics as predictors and achieve high accuracy for predicting the diameter of the largest branch per log (DLBs) and stem diameter at different heights (DS) from survey-grade drone laser scans.We show that our models are also robust and accurate when tested on suboptimal versions of the data generated by reductions in the number of points or emulations of suboptimal single-tree segmentation scenarios.Our approach provides a simple,clear,and scalable solution that can be adapted to different situations both for research and more operational mapping.

Intelligent extraction of road cracks based on vehicle laser point cloud and panoramic sequence images

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.

Building Facade Point Clouds Segmentation Based on Optimal Dual-Scale Feature Descriptors

To address the current issues of inaccurate segmentation and the limited applicability of segmentation methods for building facades in point clouds, we propose a facade segmentation algorithm based on optimal dual-scale feature descriptors. First, we select the optimal dual-scale descriptors from a range of feature descriptors. Next, we segment the facade according to the threshold value of the chosen optimal dual-scale descriptors. Finally, we use RANSAC (Random Sample Consensus) to fit the segmented surface and optimize the fitting result. Experimental results show that, compared to commonly used facade segmentation algorithms, the proposed method yields more accurate segmentation results, providing a robust data foundation for subsequent 3D model reconstruction of buildings.

Building 3D CityGML models of mining industrial structures using integrated UAV and TLS point clouds

Mining industrial areas with anthropogenic engineering structures are one of the most distinctive features of the real world.3D models of the real world have been increasingly popular with numerous applications,such as digital twins and smart factory management.In this study,3D models of mining engineering structures were built based on the CityGML standard.For collecting spatial data,the two most popular geospatial technologies,namely UAV-SfM and TLS were employed.The accuracy of the UAV survey was at the centimeter level,and it satisfied the absolute positional accuracy requirement of creat-ing all levels of detail(LoD)according to the CityGML standard.Therefore,the UAV-SfM point cloud dataset was used to build LoD 2 models.In addition,the comparison between the UAV-SfM and TLS sub-clouds of facades and roofs indicates that the UAV-SfM and TLS point clouds of these objects are highly consistent,therefore,point clouds with a higher level of detail and accuracy provided by the integration of UAV-SfM and TLS were used to build LoD 3 models.The resulting 3D CityGML models include 39 buildings at LoD 2,and two mine shafts with hoistrooms,headframes,and sheave wheels at LoD3.

Indoor Space Modeling and Parametric Component Construction Based on 3D Laser Point Cloud Data

In order to enhance modeling efficiency and accuracy,we utilized 3D laser point cloud data for indoor space modeling.Point cloud data was obtained with a 3D laser scanner and optimized with Autodesk Recap and Revit software to extract geometric information about the indoor environment.Furthermore,we proposed a method for constructing indoor elements based on parametric components.The research outcomes of this paper will offer new methods and tools for indoor space modeling and design.The approach of indoor space modeling based on 3D laser point cloud data and parametric component construction can enhance modeling efficiency and accuracy,providing architects,interior designers,and decorators with a better working platform and design reference.

A voxel-based fine-scale 3D landscape pattern analysis using laser scanner point clouds

The landscape pattern metrics can quantitatively describe the characteristics of landscape pattern and are widely used in various fields of landscape ecology.Due to the lack of vertical information,2D landscape metrics cannot delineate the vertical characteristics of landscape pattern.Based on the point clouds,a high-resolution voxel model and several voxel-based 3D landscape metrics were constructed in this study and 3D metrics calculation results were compared with that of 2D metrics.The results showed that certain quantifying difference exists between 2D and 3D landscape metrics.For landscapes with different components and spatial configurations,significant difference was disclosed between 2D and 3D landscape metrics.3D metrics can better reflect the real spatial structure characteristics of the landscape than 2D metrics.

基于知识蒸馏和定位引导的Pointpillars点云检测网络

激光雷达数据由于其几何特性,被广泛应用于三维目标检测任务中。由于点云数据的稀疏性和不规则性,难以实现特征提取的质量和推理速度间的平衡。本文提出一种基于体柱特征编码的三维目标检测算法,以Pointpillars网络为基础,设计Teacher-Student模型框架对回归框尺度进行蒸馏,增加蒸馏损失,优化训练网络模型,提升特征提取的质量。为进一步提高模型检测效果,设计定位引导分类项,增加分类预测和回归预测之间的相关性,提高物体识别准确率。本网络所做改进没有引入额外的网络嵌入。算法在KITTI数据集上的实验结果表明,相比于基准网络,在三维模式下的平均精度值从60.65%提升到了64.69%,鸟瞰图模式下的平均精度值从67.74%提升到70.24%。模型推理速度为45 FPS,在提升检测精度的同时满足了实时性要求。

Filtering of Airborne Lidar Point Clouds for Complex Cityscapes

A novel filtering algorithm for Lidar point clouds is presented, which can work well for complex cityscapes. Its main features are filtering based on raw Lidar point clouds without previous triangulation or rasterization. 3D topological relations among points are used to search edge points at the top of discontinuities, which are key information to recognize the bare earth points and building points. Experiment results show that the proposed algorithm can preserve discontinuous features in the bare earth and has no impact of size and shape of buildings.

Landslide data mosaicking based on an airborne laser point cloud and multi-beam sonar images

Landslides are one of the most disastrous geological hazards in southwestern China.Once a landslide becomes unstable,it threatens the lives and safety of local residents.However,empirical studies on landslides have predominantly focused on landslides that occur on land.To this end,we aim to investigate ashore and underwater landslide data synchronously.This study proposes an optimized mosaicking method for ashore and underwater landslide data.This method fuses an airborne laser point cloud with multi-beam depth sounder images.Owing to their relatively high efficiency and large coverage area,airborne laser measurement systems are suitable for emergency investigations of landslides.Based on the airborne laser point cloud,the traversal of the point with the lowest elevation value in the point set can be used to perform rapid extraction of the crude channel boundaries.Further meticulous extraction of the channel boundaries is then implemented using the probability mean value optimization method.In addition,synthesis of the integrated ashore and underwater landslide data angle is realized using the spatial guide line between the channel boundaries and the underwater multibeam sonar images.A landslide located on the right bank of the middle reaches of the Yalong River is selected as a case study to demonstrate that the proposed method has higher precision thantraditional methods.The experimental results show that the mosaicking method in this study can meet the basic needs of landslide modeling and provide a basis for qualitative and quantitative analysis and stability prediction of landslides.

A Novel Airborne 3D Laser Point Cloud Hole Repair Algorithm Considering Topographic Features

Hole repair processing is an important part of point cloud data processing in airborne 3-dimensional(3D)laser scanning technology.Due to the fragmentation and irregularity of the surface morphology,when applying the 3D laser scanning technology to mountain mapping,the conventional mathematical cloud-based point cloud hole repair method is not ideal in practical applications.In order to solve this problem,we propose to repair the valley and ridge line first,and then repair the point cloud hole.The main technical steps of the method include the following points:First,the valley and ridge feature lines are extracted by the GIS slope analysis method;Then,the valley and ridge line missing from the hole are repaired by the mathematical interpolation method,and the repaired results are edited and inserted to the original point cloud;Finally,the traditional repair method is used to repair the point cloud hole whose valley line and ridge line have been repaired.Three experiments were designed and implemented in the east bank of the Xiaobaini River to test the performance of the proposed method.The results showed that compared with the direct point cloud hole repair method in Geomagic Studio software,the average repair accuracy of the proposed method,in the 16 m buffer zone of valley line and ridge line,is increased from 56.31 cm to 31.49 cm.The repair performance is significantly improved.

Automated registration of wide-baseline point clouds in forests using discrete overlap search

Forest is one of the most challenging environments to be recorded in a three-dimensional(3D)digitized geometrical representation,because of the size and the complexity of the environment and the data-acquisition constraints brought by on-site conditions.Previous studies have indicated that the data-acquisition pattern can have more influence on the registration results than other factors.In practice,the ideal short-baseline observations,i.e.,the dense collection mode,is rarely feasible,considering the low accessibility in forest environments and the commonly limited labor and time resources.The wide-baseline observations that cover a forest site using a few folds less observations than short-baseline observations,are therefore more preferable and commonly applied.Nevertheless,the wide-baseline approach is more challenging for data registration since it typically lacks the required sufficient overlaps between datasets.Until now,a robust automated registration solution that is independent of special hardware requirements has still been missing.That is,the registration accuracy is still far from the required level,and the information extractable from the merged point cloud using automated registration could not match that from the merged point cloud using manual registration.This paper proposes a discrete overlap search(DOS)method to find correspondences in the point clouds to solve the low-overlap problem in the wide-baseline point clouds.The proposed automatic method uses potential correspondences from both original data and selected feature points to reconstruct rough observation geometries without external knowledge and to retrieve precise registration parameters at data-level.An extensive experiment was carried out with 24 forest datasets of different conditions categorized in three difficulty levels.The performance of the proposed method was evaluated using various accuracy criteria,as well as based on data acquired from different hardware,platforms,viewing perspectives,and at different points of time.The proposed method achieved a 3D registration accuracy at a 0.50-cm level in all difficulty categories using static terrestrial acquisitions.In the terrestrial-aerial registration,data sets were collected from different sensors and at different points of time with scene changes,and a registration accuracy at the raw data geometric accuracy level was achieved.These results represent the highest automated registration accuracy and the strictest evaluation so far.The proposed method is applicable in multiple scenarios,such as 1)the global positioning of individual under-canopy observations,which is one of the main challenges in applying terrestrial observations lacking a global context,2)the fusion of point clouds acquired from terrestrial and aerial perspectives,which is required in order to achieve a complete forest observation,3)mobile mapping using a new stop-and-go approach,which solves the problems of lacking mobility and slow data collection in static terrestrial measurements as well as the data-quality issue in the continuous mobile approach.Furthermore,this work proposes a new error estimate that units all parameter-level errors into a single quantity and compensates for the downsides of the widely used parameter-and object-level error estimates;it also proposes a new deterministic point sets registration method as an alternative to the popular sampling methods.

基于改进PointPillars的点云车辆目标检测

针对自动驾驶领域中,激光雷达获取的点云数据存在稀疏性以及边缘噪点误检等问题,提出一种基于改进PointPil-lars的点云车辆目标检测方法。首先,基于SimAM注意力机制改进体素化特征输入,使得网络特征提取阶段能更加关注关键信息,提高特征学习的全局性。其次,基于卷积块注意力模块(CBAM)改进骨干网络结构,提出全新的轻量化通道注意力模块Tiny-CAM和可变形空间注意力模块Deformable-SAM,构建Multi-CBAM骨干网络,提升网络特征提取及特征融合能力。在KITTI数据集以及非公开车库点云数据集上进行验证,实验结果表明,与原网络相比,改进PointPillars方法具有更高的检测精度,平均检测精度提升2.98%,针对遮挡小于30%的点云车辆目标检测精度提升6.51%,证明了该方法的有效性。

Pre-process algorithm for satellite laser ranging data based on curve recognition from points cloud

The satellite laser ranging （SLR） data quality from the COMPASS was analyzed, and the difference between curve recognition in computer vision and pre-process of SLR data finally proposed a new algorithm for SLR was discussed data based on curve recognition from points cloud is proposed. The results obtained by the new algorithm are 85 % （or even higher） consistent with that of the screen displaying method, furthermore, the new method can process SLR data automatically, which makes it possible to be used in the development of the COMPASS navigation system.

Cloud Detection and Centroid Extraction of Laser Footprint Image of GF-7 Satellite Laser Altimetry

The laser altimeter loaded on the GaoFen-7(GF-7)satellite is designed to record the full waveform data and footprint image,which can obtain high-precision elevation control points for stereo image.The footprint camera equipped on the GF-7 laser altimetry system can capture the energy distribution at the time of laser emission and the image of the ground object where the laser falls,which can be used to judge whether the laser is affected by the cloud.At the same time,the centroid of laser spot on the footprint image can be extracted to monitor the change of laser pointing stability.In this manuscript,a data quality analysis scheme of laser altimetry based on footprint image is presented.Firstly,the cloud detection of footprint image is realized based on deep learning.The fusion result of the model is about 5%better than that of the traditional cloud detection algorithm,which can quickly and accurately determine whether the laser spot is affected by cloud.Secondly,according to the characteristics of footprint image,a threshold constrained ellipse fitting method for extracting the centroid of laser spot is proposed to monitor the pointing stability of long-period lasers.Based on the above method,the change of laser spot centroid since GF-7 satellite was put into operation is analyzed,and the conclusions obtained have certain reference significance for the quality control of satellite laser altimetry data and the analysis of pointing angle stability.

改进PointNet++模型在道路杆状物提取中的应用

针对现有道路杆状物提取大多需要针对数据类型人工设计特征、泛用性差、自动化程度低等问题,本文提出一种基于改进PointNet++深度学习网络的道路杆状物语义分割方法,实现了对道路杆状物的提取。首先对原网络模型的感受野、分块大小等参数进行调整,使得该模型更适合道路点云数据;然后针对点云数据不平衡的问题,采用焦点损失函数作为模型的损失函数,使占比较少的类别得到充分训练;最后针对PointNet++网络提取特征时没有考虑邻域内各点特征影响关系的问题,采用邻域特征聚合模块融合邻域信息,提升该网络模型对点云特征的学习能力。为验证所提方法的有效性,使用改进后的网络模型在自建的道路点云组成的数据集上进行了试验,相对于经典PointNet++网络,杆状物类的分割精度明显提升,在简单道路和复杂道路上的交并比(IoU)分别提升了8.44%、15.25%,达到了98.88%、92.50%。

Usage of 3D Point Cloud Data in BIM （Building Information Modelling）： Current Applications and Challenges

BIM （building information modelling） has gained wider acceptance in the A/E/C （architecture/engineering/construction） industry in the US and internationally. This paper presents current industry approaches of implementing 3D point cloud data in BIM and VDC （virtual design and construction） applications during various stages of a project life cycle and the challenges associated with processing the huge amount of 3D point cloud data. Conversion from discrete 3D point cloud raster data to geometric/vector BIM data remains to be a labor-intensive process. The needs for intelligent geometric feature detection/reconstruction algorithms for automated point cloud processing and issues related to data management are discussed. This paper also presents an innovative approach for integrating 3D point cloud data with BIM to efficiently augment built environment design, construction and management.

Methodology for Extraction of Tunnel Cross-Sections Using Dense Point Cloud Data

Tunnel deformation monitoring is a crucial task to evaluate tunnel stability during the metro operation period.Terrestrial Laser Scanning(TLS)can collect high density and high accuracy point cloud data in a few minutes as an innovation technique,which provides promising applications in tunnel deformation monitoring.Here,an efficient method for extracting tunnel cross-sections and convergence analysis using dense TLS point cloud data is proposed.First,the tunnel orientation is determined using principal component analysis(PCA)in the Euclidean plane.Two control points are introduced to detect and remove the unsuitable points by using point cloud division and then the ground points are removed by defining an elevation value width of 0.5 m.Next,a z-score method is introduced to detect and remove the outlies.Because the tunnel cross-section’s standard shape is round,the circle fitting is implemented using the least-squares method.Afterward,the convergence analysis is made at the angles of 0°,30°and 150°.The proposed approach’s feasibility is tested on a TLS point cloud of a Nanjing subway tunnel acquired using a FARO X330 laser scanner.The results indicate that the proposed methodology achieves an overall accuracy of 1.34 mm,which is also in agreement with the measurements acquired by a total station instrument.The proposed methodology provides new insights and references for the applications of TLS in tunnel deformation monitoring,which can also be extended to other engineering applications.

Study on the LAI Single Tree Model Based on Terrestrial Laser Scanning

Leaf area index (LAI) is a key parameter for studying global terrestrial ecology and environment and has great ecological significance. How to accurately measure and calculate structural parameters of trees has become an urgent matter. This paper reports the use of terrestrial laser scanning (TLS) as a measurement tool to achieve accurate LAI estimation through point cloud preprocessing measures, the LeWos algorithm, and voxel methods. The accuracy and feasibility of this indirect measurement method were explored. It is found that the single wood structure parameters extracted from TLS have a good linear relationship with manual measurement, and the extraction errors meet the requirements of real-scene conversion. The study also found when the voxel size is consistent with the minimum distance of the point cloud set by TLS instrument, it has a strong correlation with the measured value of canopy analyser. These results lay the foundation for conveniently and quickly obtaining structural parameters of trees, tree growth state detection, and canopy ecological benefit assessment.

基于多线激光雷达的井下斜坡道无人矿卡定位与建图方法

井下斜坡道的定位与建图是实现井下斜坡道无人驾驶的关键技术之一,矿山井下斜坡道区域为典型非结构化环境特征,且道路具有一定倾斜角度,采用传统SLAM算法无法获得精确里程计信息,导致定位与建图精度难以满足无人矿卡行驶需求。针对上述问题,通过研究激光SLAM(Simultaneous Localization And Mapping)算法LeGO-LOAM,笔者提出一种适用于矿山井下斜坡道环境的定位与建图方法。首先,针对井下斜坡道口两侧均为光滑水泥墙壁,特征点稀少问题,设计了基于人工路标的辅助增强定位方法,有效增加点云特征数量,从而优化位姿估计结果,避免建图漂移现象;然后在特征预处理阶段,提出了一种基于激光点云高度差与坡度信息融合的提取地面点高效算法,通过改善地面地点的选取策略,针对倾斜坑洼路面仍能有效识别地面点,解决了井下斜坡道定位与建图倾斜角度大、误差大等问题;其次,基于CVC(Curved-Voxel Clustering)聚类算法设计了一种斜坡道点云曲率体素聚类算法,采用曲率体素和基于哈希的数据结构对点云进行分割,大幅提高在井下稀疏、噪声环境下点云聚类的鲁棒性;最后,运用Scan-To-Map进行点云匹配,同时兼顾点云配准的性能与速度。在中钢集团山东某井下斜坡道的现场实验证明:与原算法相比精度提升13.15%,Z轴误差降低22.3%,地图质量明显提升,能有效解决井下无人驾驶建图及定位的难题。

Research on 3D Laser Scanning Reconstruction of Ancient Buildings Combined with BIM Technology

After more than 30 years of scientific and social development, surveying and mapping technology by leaps and bounds, engineering surveying technology has undergone tremendous changes. In the process of protecting ancient buildings, it is necessary to obtain the precise dimensions of architectural details. In this study, the path of 3D laser scanning combined with BIM technology is explored. Taking the observation and protection of the ancestral hall of the Liu family as an example, this study aims to draw drawings that reflect the relevant information about the ancient buildings, the accurate three-dimensional model of ancient buildings is established with BIM technology, which provides new methods and ideas for the research and protection of ancient buildings. .