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lidar-based estimation of bounding box coordinates using Gaussian process regression and particle swarm optimization

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Biomimetic Intelligence & Robotics 2024年第1期4卷 24-35页

作者： Vinodha K. E.S.Gopi Tushar Agnibhoj Department of Electronics and Communication Engineering National Institute of Technology TiruchirappalliTamil Nadu620015India

Camera-based object tracking systems in a given closed environment lack privacy and confidentiality.In this study,light detection and ranging(lidar)was applied to track objects similar to the camera tracking in a closed environment,guaranteeing privacy and confidentiality.The primary objective was to demonstrate the efficacy of the proposed technique through carefully designed experiments conducted using two scenarios.In Scenario I,the study illustrates the capability of the proposed technique to detect the locations of multiple objects positioned on a flat surface,achieved by analyzing lidar data collected from several locations within the closed environment.Scenario II demonstrates the effectiveness of the proposed technique in detecting multiple objects using lidar data obtained from a single,fixed location.Real-time experiments are conducted with human subjects navigating predefined paths.Three individuals move within an environment,while lidar,fixed at the center,dynamically tracks and identifies their locations at multiple instances.Results demonstrate that a single,strategically positioned lidar can adeptly detect objects in motion around it.Furthermore,this study provides a comparison of various regression techniques for predicting bounding box coordinates.Gaussian process regression(GPR),combined with particle swarm optimization(PSO)for prediction,achieves the lowest prediction mean square error of all the regression techniques examined at 0.01.Hyperparameter tuning of GPR using PSO significantly minimizes the regression error.Results of the experiment pave the way for its extension to various real-time applications such as crowd management in malls,surveillance systems,and various Internet of Things scenarios.

关键词： lidar Data acquisition Bounding box Gaussian process regression Particle swarm optimization(PSO)

Collaborative positioning for swarms:A brief survey of vision,lidar and wireless sensors based methods

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Defence Technology（防务技术） 2024年第3期33卷 475-493页

作者： Zeyu Li Changhui Jiang Xiaobo Gu Ying Xu Feng zhou Jianhui Cui College of Geodesy and Geomatics Shandong University of Science and TechnologyQingdao266590China Geoloc Laboratory Universite Gustave EiffelParis77454France School of Automation Guangdong University of TechnologyGuangzhou510006China

As positioning sensors,edge computation power,and communication technologies continue to develop,a moving agent can now sense its surroundings and communicate with other agents.By receiving spatial information from both its environment and other agents,an agent can use various methods and sensor types to localize itself.With its high flexibility and robustness,collaborative positioning has become a widely used method in both military and civilian applications.This paper introduces the basic fundamental concepts and applications of collaborative positioning,and reviews recent progress in the field based on camera,lidar(Light Detection and Ranging),wireless sensor,and their integration.The paper compares the current methods with respect to their sensor type,summarizes their main paradigms,and analyzes their evaluation experiments.Finally,the paper discusses the main challenges and open issues that require further research.

关键词： Collaborative positioning Vision lidar Wireless sensors Sensor fusion

基于改进PointNet++的lidar点云分割模型

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激光与光电子学进展 2024年第4期61卷 111-118页

作者：张驰王志杰吴昊陈动南京林业大学土木工程学院江苏南京210037

针对PointNet++在特征提取阶段未能深层挖掘lidar点云的语义特征及其在特征聚合阶段采用最大池化聚合导致特征丢失,进而导致点云分割精度下降的问题,通过改进PointNet++的特征提取及特征聚合模块,提出一种基于特征偏差值和注意力机制的... 详细信息

针对PointNet++在特征提取阶段未能深层挖掘lidar点云的语义特征及其在特征聚合阶段采用最大池化聚合导致特征丢失,进而导致点云分割精度下降的问题,通过改进PointNet++的特征提取及特征聚合模块,提出一种基于特征偏差值和注意力机制的点云分割模型。首先,利用球形采样获取不同的局部邻域,并采用K最近邻(KNN)算法筛选邻域点,计算不同邻域的特征偏差值,获取点云的深层语义信息,增强模型对不同局部邻域的识别能力;其次,利用基于注意力机制的特征聚合模块代替PointNet++中的最大池化模块,在聚合特征阶段学习不同特征的权重,从而提高模型对不同结构信息的筛选能力,增强模型的分割性能;最后,为了进一步优化模型架构,在全连接层中加入残差模块,共享权重,避免参数冗余,提升模型性能。基于ISPRS提供的Vaihingen数据集与斯坦福的S3DIS数据集进行实验验证,实验结果表明,所提模型总体精度达到86.69%,较PointNet++提高了5.49个百分点,同时平均F1得分达到了73.97%,较PointNet++提高了8.30个百分点。在S3DIS数据集上的实验结果表明,与PointNet++、RandLA-Net和ConvPoint等主流模型相比,所提模型结果较PointNet++也有提升,即相较于PointNet++的分割结果,改进后的模型能够充分提取点云的语义特征,有效提高模型分割精度。

关键词：点云分割 PointNet++ lidar 特征偏差值注意力机制特征融合残差结构

基于lidar数据的汕尾火山嶂地质灾害风险评价

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中山大学学报（自然科学版）（中英文） 2024年第1期63卷 10-23页

作者：甄俊伟黄智炜章桂芳曾探王同皓广东省地质局第七地质大队广东惠州516000 中山大学地球科学与工程学院广东珠海519082 广东省地质过程与矿产资源探查实验室广东珠海519082 南方海洋科学与工程广东省实验室(珠海) 广东珠海519082

机载激光雷达(lidar,light detection and ranging)数据能有效去除植被,获取真实的地表形态,从而为植被覆盖区的地质灾害风险评价提供新的方法和手段。汕尾火山嶂山体陡峻、植被茂密,是滑坡、崩塌和泥石流的易发地,本文首先采用高分辨率... 详细信息

机载激光雷达(lidar,light detection and ranging)数据能有效去除植被,获取真实的地表形态,从而为植被覆盖区的地质灾害风险评价提供新的方法和手段。汕尾火山嶂山体陡峻、植被茂密,是滑坡、崩塌和泥石流的易发地,本文首先采用高分辨率lidar数据生成高精度DEM数据以及坡度、坡向、曲率、起伏度、粗糙度和山体阴影等地形因子,综合高分一号遥感影像进行滑坡/崩塌解译共获得滑坡/崩塌44处;然后基于变维分形模型确定各解译因子对滑坡/崩塌形成的权重后计算获得每个解译滑坡/崩塌的确认概率,剔除概率较低的滑坡/崩塌3处;最后根据沟谷特征将火山嶂划分为6个子区,基于各个子区的地形特征、滑坡/崩塌密度和体量以及人类活动分布进行地质灾害风险评价。结果表明基于lidar数据生成的高精度地形因子可以有效地去除植被影响,是植被覆盖区地质灾害解译的有效手段。

关键词：地质灾害 lidar 风险评价火山嶂

Deforestation in Latin America in the 2000s predominantly occurred outside of typical mature forests

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The Innovation 2024年第3期5卷 88-98页

作者： Zhiyu Zhang Wenjian Ni Shaun Quegan Jingming Chen Peng Gong Luiz Carlos Estraviz Rodriguez Huadong Guo Jiancheng Shi Liangyun Liu Zengyuan Li Yating He Qinhuo Liu Yosio Shimabukuro Guoqing Sun Key Laboratory of Remote Sensing and Digital Earth Chinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Shijingshan DistrictBeijing 100049China Chinal of Mathematics and Statistics University of SheffieldSheffield S37RHUK Department of Geography and Program in Planning University of TorontoTorontoON M5S 3G3Canada Department of Earth Sciences and Department of Geography University of Hong KongHong KongChina Forest Science Department Luiz de Queiroz College of AgricultureUniversity of São PauloAv.Pádua Dias11Piracicaba 13418-900Brazil National Space Science Center Chinese Academy of SciencesBeijing 100190China Institute of Forest Resource Information Techniques Chinese Academy of ForestryBeijing 100091China Research Institute of Forest Policy and Information Chinese Academy of ForestryBeijing 100091China Remote Sensing Department National Institute for Space Research(INPE)Av.dos Astronautas 1758São Josédos Campos 12227-010Brazil Department of Geographical Sciences University of MarylandCollege ParkMD 20742USA

The role of tropical forests in the global carbon budget remains controversial,as carbon emissions from deforestation are highly uncertain.This high uncertainty arises from the use of either fixed forest carbon stock density or maps generated from satellite-based optical reflectance with limited sensitivity to biomass to generate accurate estimates of emissions from deforestation.New space missions aiming to accurately map the carbon stock density rely on direct measurements of the spatial structures of forests using lidar and radar.We found that lost forests are special cases,and their spatial structures can be directly measured by combining archived data acquired before and after deforestation by space missions principally aimed at measuring topography.Thus,using biomass mapping,we obtained new estimates of carbon loss from deforestation ahead of forthcoming space missions.Here,using a high-resolution map of forest loss and the synergy of radar and lidar to estimate the aboveground biomass density of forests,we found that deforestation in the 2000s in Latin America,one of the severely deforested regions,mainly occurred in forests with a significantly lower carbon stock density than typical mature forests.

关键词： forest Latin lidar

Semi-automated detection of rangeland runoff and erosion control berms using high-resolution topography data

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International Soil and Water Conservation Research 2024年第1期12卷 217-226页

作者： Li Li Changjiang River Scientific Research Institute of Changjiang Water Resources Commission Wuhan430010China Research Center on Mountain Torrent&Geologic Disaster Prevention Ministry of Water ResourcesWuhan430010China University of Arizona School of Natural Resources and the EnvironmentTucsonAZ85719USA

An inventory of topographic modifications is essential to addressing their impacts on hydrological and morphological processes in human-altered watersheds.However,such inventories are generally lacking.This study presents two workflows for semi-automatic detection of linear earthen runoff and erosion control berms in rangelands using high-resolution topographic data.The workflows consist of initial object identification by applying either morphological grayscale reconstruction(MGR)or the Geomorphon(GEO)method,followed by identification refinements through filters based on objects’horizontal and vertical information.Three sites were selected within the Altar Valley,Arizona,in the southwestern United States.One site was used for developing workflows and optimizing filter thresholds,and the other two sites were used to validate workflows.The results showed that:1)The MGR-based workflow methodology could produce final precision and detection rates of up to 92%and 75%,respectively,and take less than 5 s for a 10.1 km^(2) site;2)The workflow based on the MGR method yielded greater identification accuracy than did the GEO workflow;3)Object length,orientation,and eccentricity were important characteristics for identifying earthen berms,and are sensitive to general channel flow direction and berm shape;4)Manual interrogation of topographic data and imagery can significantly improve identification precision rates.The proposed workflows will be useful for developing inventories of runoff and erosion control structures in support of sustainable rangeland management.

关键词： Earthen berm Rangeland Grayscale morphological reconstruction Geomorphon lidar

Photonic integrated optical phased arrays and their applications[Invited]

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Chinese Optics Letters 2024年第2期22卷 42-55页

作者：马志鹏万远剑梁航付瑶唐国彪赵晓阳赵世傲匡海波张宇王健 Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China Optics Valley Laboratory Wuhan 430074China

In recent years,optical phased arrays(OPAs)have attracted great interest for their potential applications in light detection and ranging(Li DAR),free-space optical communications(FSOs),holography,and so on.Photonic integrated circuits(PICs)provide solutions for further reducing the size,weight,power,and cost of OPAs.In this paper,we review the recent development of photonic integrated OPAs.We summarize the typical architecture of the integrated OPAs and their performance.We analyze the key components of OPAs and evaluate the figure of merit for OPAs.Various applications in Li DAR,FSO,imaging,biomedical sensing,and specialized beam generation are introduced.

关键词： optical phased arrays lidar silicon photonics beam steering photonic integration

Deep learning-based semantic segmentation of human features in bath scrubbing robots

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Biomimetic Intelligence & Robotics 2024年第1期4卷 70-79页

作者： Chao Zhuang Tianyi Ma Bokai Xuan Cheng Chang Baichuan An Minghuan Yin Hao Sun School of Artificial Intelligence and Data Science Hebei University of TechnologyTianjin 300401China Engineering Research Center of Intelligent Rehabilitation Device and Detection Technology Ministry of EducationTianjin 300401China

With the rise in the aging population,an increase in the number of semidisabled elderly individuals has been noted,leading to notable challenges in medical and healthcare,exacerbated by a shortage of nursing staff.This study aims to enhance the human feature recognition capabilities of bath scrubbing robots operating in a water fog environment.The investigation focuses on semantic segmentation of human features using deep learning methodologies.Initially,3D point cloud data of human bodies with varying sizes are gathered through light detection and ranging to establish human models.Subsequently,a hybrid filtering algorithm was employed to address the impact of the water fog environment on the modeling and extraction of human regions.Finally,the network is refined by integrating the spatial feature extraction module and the channel attention module based on PointNet.The results indicate that the algorithm adeptly identifies feature information for 3D human models of diverse body sizes,achieving an overall accuracy of 95.7%.This represents a 4.5%improvement compared with the PointNet network and a 2.5%enhancement over mean intersection over union.In conclusion,this study substantially augments the human feature segmentation capabilities,facilitating effective collaboration with bath scrubbing robots for caregiving tasks,thereby possessing significant engineering application value.

关键词： 3D point cloud Human model lidar Semantic segmentation

基于lidar点云的单根电力线分离提取与多维度拟合重建

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智能计算机与应用 2024年第1期14卷 119-123,129页

作者：毛建国卿粼波池慧玲四川大学电子信息学院成都610065

激光雷达技术能够快速高效地获取高精度的三维点云数据,机载lidar不受天气影响且能在复杂地理条件下工作,已成为电力线巡检的重要方法。而从获取的lidar点云数据中高效提取架空电力线点云是机载lidar电力巡检后期数据处理的重要内容。... 详细信息

激光雷达技术能够快速高效地获取高精度的三维点云数据,机载lidar不受天气影响且能在复杂地理条件下工作,已成为电力线巡检的重要方法。而从获取的lidar点云数据中高效提取架空电力线点云是机载lidar电力巡检后期数据处理的重要内容。本文基于某地输电线走廊的lidar点云数据,设计了一套单根电力线分离提取和多维度拟合重建的方案。该方案首先使用统计滤波算法去除lidar数据中的异常离群点;再采用布料模拟滤波算法,结合数据高程信息去除地面点,利用PCA主成分分析法与快速欧式聚类算法,从电力线走廊点云中分离提取单根电力线;最后基于提取结果对单根电力线进行多维度拟合重建。实验结果表明,本文方法能精准且快速地分离提取单根电力线并进行多维度拟合重建,在电力线智能巡检中具有良好的工程应用价值。

关键词： lidar 快速欧式聚类电力线提取三维重建