LiDAR and camera are two of the most common sensors used in the fields of robot perception,autonomous driving,augmented reality,and virtual reality,where these sensors are widely used to perform various tasks such as ...LiDAR and camera are two of the most common sensors used in the fields of robot perception,autonomous driving,augmented reality,and virtual reality,where these sensors are widely used to perform various tasks such as odometry estimation and 3D reconstruction.Fusing the information from these two sensors can significantly increase the robustness and accuracy of these perception tasks.The extrinsic calibration between cameras and LiDAR is a fundamental prerequisite for multimodal systems.Recently,extensive studies have been conducted on the calibration of extrinsic parameters.Although several calibration methods facilitate sensor fusion,a comprehensive summary for researchers and,especially,non-expert users is lacking.Thus,we present an overview of extrinsic calibration and discuss diverse calibration methods from the perspective of calibration system design.Based on the calibration information sources,this study classifies these methods as target-based or targetless.For each type of calibration method,further classification was performed according to the diverse types of features or constraints used in the calibration process,and their detailed implementations and key characteristics were introduced.Thereafter,calibration-accuracy evaluation methods are presented.Finally,we comprehensively compare the advantages and disadvantages of each calibration method and suggest directions for practical applications and future research.展开更多
Here we report on simultaneous lidar observations of sporadic Ni(Nis)layers and sporadic Na(Nas)layers in the atmosphere over Yanqing,Beijing(40.42°N,116.02°E)from April 2019 to October 2022.During 343 night...Here we report on simultaneous lidar observations of sporadic Ni(Nis)layers and sporadic Na(Nas)layers in the atmosphere over Yanqing,Beijing(40.42°N,116.02°E)from April 2019 to October 2022.During 343 nights of observation,68 Nis and 56 Nas were observed.The seasonal variation of Nis and Nas was also obtained,with the highest occurrence of Nis being in July(43%)and that of Nas being in June(61%).We found that the seasonal variation of Nis is similar to that of Nas and that both occur more frequently in summer than in winter.In addition,we found 23 events in which Nis and Nas occur simultaneously.The average peak altitude of Nas is approximately 1 km higher than that of Nis,and the peak density ratio of Nas to Nis is approximately 5,which is half the density ratio of the two main layers.Additionally,the strength factor for Nas is smaller than that for Nis.Through data analysis of sporadic E layers(Es),we found that Nis and Nas has a significant correlation with Es.The neutralization rates of Ni^(+)/Na^(+)were calculated according to the dissociative recombination reaction of Ni^(+)/Na^(+)and the WACCM-Ni(Whole Atmosphere Community Climate Model of Ni).The production rates of Ni and Na were estimated to be approximately 1:4.4,which is consistent with the density ratio of Nis to Nas.The results showed that the neutralization reaction of Ni+,Na+,and electrons in Es is the main reason for the formation of the Nis layer and the Nas layer.展开更多
针对复杂河道治理中传统测绘技术高程精度不足和纹理细节缺失的矛盾,本文提出一种基于无人机激光雷达(LightLaser Detection and Ranging,LiDAR)和倾斜摄影的动态加权融合技术。该技术通过三级融合流程构建真彩色三维模型,采用权迭代法...针对复杂河道治理中传统测绘技术高程精度不足和纹理细节缺失的矛盾,本文提出一种基于无人机激光雷达(LightLaser Detection and Ranging,LiDAR)和倾斜摄影的动态加权融合技术。该技术通过三级融合流程构建真彩色三维模型,采用权迭代法优化特征匹配,并基于先验试验开发动态权重系数模型,以自适应调整LiDAR和影像权重。试验表明,融合数据在高植被区、工程密集区冲淤量计算误差小于5%,比传统方法效率高60%。工程应用表明,该技术在山东省某河道治理项目中实现成本降低60%、工期缩短20%的目标,有效解决了单一技术在复杂河道场景下的应用局限,具有重要的工程应用价值。展开更多
基于背包式激光雷达(light detection and ranging, Li DAR)点云数据,探究其在校园实景建筑物提取中的应用,旨在提升校园场景建筑物提取效率。在明确数据处理的基础上,通过点云滤波、分割等处理实现建筑物提取,并通过实验对比不同方法...基于背包式激光雷达(light detection and ranging, Li DAR)点云数据,探究其在校园实景建筑物提取中的应用,旨在提升校园场景建筑物提取效率。在明确数据处理的基础上,通过点云滤波、分割等处理实现建筑物提取,并通过实验对比不同方法的提取效果,证实基于背包式Li DAR点云+成分分析法所提取的建筑物点云精准度较高,可为校园规划、教学运行、校园安防等提供可靠的数据支持。展开更多
基金Supported by Beijing Natural Science Foundation(Grant No.L241012)the National Natural Science Foundation of China(Grant No.62572468).
文摘LiDAR and camera are two of the most common sensors used in the fields of robot perception,autonomous driving,augmented reality,and virtual reality,where these sensors are widely used to perform various tasks such as odometry estimation and 3D reconstruction.Fusing the information from these two sensors can significantly increase the robustness and accuracy of these perception tasks.The extrinsic calibration between cameras and LiDAR is a fundamental prerequisite for multimodal systems.Recently,extensive studies have been conducted on the calibration of extrinsic parameters.Although several calibration methods facilitate sensor fusion,a comprehensive summary for researchers and,especially,non-expert users is lacking.Thus,we present an overview of extrinsic calibration and discuss diverse calibration methods from the perspective of calibration system design.Based on the calibration information sources,this study classifies these methods as target-based or targetless.For each type of calibration method,further classification was performed according to the diverse types of features or constraints used in the calibration process,and their detailed implementations and key characteristics were introduced.Thereafter,calibration-accuracy evaluation methods are presented.Finally,we comprehensively compare the advantages and disadvantages of each calibration method and suggest directions for practical applications and future research.
基金supported by the Specialized Research Fund for State Key Laboratories,Chinese Meridian Project,the Specialized Research Fund for the State Key Laboratory of Solar Activity and Space Weather,postgraduate Education Reform and Quality Improvement Project of Henan Province(Grant No.YJS2024JD32)Natural Science Foundation Project of Henan Province(Grant No.242300420253)National Natural Science Foundation of China for Young Scientists(Grant No.42504156)funding.
文摘Here we report on simultaneous lidar observations of sporadic Ni(Nis)layers and sporadic Na(Nas)layers in the atmosphere over Yanqing,Beijing(40.42°N,116.02°E)from April 2019 to October 2022.During 343 nights of observation,68 Nis and 56 Nas were observed.The seasonal variation of Nis and Nas was also obtained,with the highest occurrence of Nis being in July(43%)and that of Nas being in June(61%).We found that the seasonal variation of Nis is similar to that of Nas and that both occur more frequently in summer than in winter.In addition,we found 23 events in which Nis and Nas occur simultaneously.The average peak altitude of Nas is approximately 1 km higher than that of Nis,and the peak density ratio of Nas to Nis is approximately 5,which is half the density ratio of the two main layers.Additionally,the strength factor for Nas is smaller than that for Nis.Through data analysis of sporadic E layers(Es),we found that Nis and Nas has a significant correlation with Es.The neutralization rates of Ni^(+)/Na^(+)were calculated according to the dissociative recombination reaction of Ni^(+)/Na^(+)and the WACCM-Ni(Whole Atmosphere Community Climate Model of Ni).The production rates of Ni and Na were estimated to be approximately 1:4.4,which is consistent with the density ratio of Nis to Nas.The results showed that the neutralization reaction of Ni+,Na+,and electrons in Es is the main reason for the formation of the Nis layer and the Nas layer.
文摘针对复杂河道治理中传统测绘技术高程精度不足和纹理细节缺失的矛盾,本文提出一种基于无人机激光雷达(LightLaser Detection and Ranging,LiDAR)和倾斜摄影的动态加权融合技术。该技术通过三级融合流程构建真彩色三维模型,采用权迭代法优化特征匹配,并基于先验试验开发动态权重系数模型,以自适应调整LiDAR和影像权重。试验表明,融合数据在高植被区、工程密集区冲淤量计算误差小于5%,比传统方法效率高60%。工程应用表明,该技术在山东省某河道治理项目中实现成本降低60%、工期缩短20%的目标,有效解决了单一技术在复杂河道场景下的应用局限,具有重要的工程应用价值。
文摘基于背包式激光雷达(light detection and ranging, Li DAR)点云数据,探究其在校园实景建筑物提取中的应用,旨在提升校园场景建筑物提取效率。在明确数据处理的基础上,通过点云滤波、分割等处理实现建筑物提取,并通过实验对比不同方法的提取效果,证实基于背包式Li DAR点云+成分分析法所提取的建筑物点云精准度较高,可为校园规划、教学运行、校园安防等提供可靠的数据支持。
