Atmospheric CO_(2) concentrations are predominantly regulated by multiple emission sources,with industrial emis-sions representing a critical anthropogenic driver that significantly influences temporal and spatial het...Atmospheric CO_(2) concentrations are predominantly regulated by multiple emission sources,with industrial emis-sions representing a critical anthropogenic driver that significantly influences temporal and spatial heterogeneity in regional CO_(2) patterns.This study investigated the spatiotemporal distribution of atmospheric CO_(2) in Pucheng and Nanping industrial parks,Nanping City,by conducting field experiments using two coherent differential absorption lidars from 1 August to 31 October 2024.Results showed that the spatial distributions of CO_(2) emis-sions within a 3 km radius were mapped,and the local diffusion processes were clarified.CO_(2) patterns varied differently in two industrial parks over the three-month period:Average CO_(2) concentrations in non-emission areas were 422.4 ppm in Pucheng and 408.7 ppm in Nanping,with the former experiencing higher and more variable carbon emissions;Correlation analysis indicated that synthetic leather factories in Pucheng contributed more to SO_(2) and NO_(x) levels compared to the chemical plant in Nanping;In Pucheng,CO_(2) concentrations were transported from the north at ground-level wind speeds exceeding 4 m/s,while in Nanping,the concentrations dispersed gradually with increasing wind speeds;Forward trajectory simulations revealed that the peak-emission from Pucheng primarily affected southern Fujian,northeastern Jiangxi,and southern Anhui,while the peak-emission from Nanping influenced central and western Fujian and northeastern Jiangxi.Besides,emissions in both industrial parks were higher on weekdays and lower on weekends,reflecting changes in industrial activi-ties.The study underscores the potential of lidar technology for providing detailed insights into CO_(2) distribution and the interactions between emissions,wind patterns,and carbon transport.展开更多
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
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 the National Natural Science Foundation of China(Nos.42305147 and 42405138)the Natural Science Foundation of Jiangsu Province(No.BK20230428).
文摘Atmospheric CO_(2) concentrations are predominantly regulated by multiple emission sources,with industrial emis-sions representing a critical anthropogenic driver that significantly influences temporal and spatial heterogeneity in regional CO_(2) patterns.This study investigated the spatiotemporal distribution of atmospheric CO_(2) in Pucheng and Nanping industrial parks,Nanping City,by conducting field experiments using two coherent differential absorption lidars from 1 August to 31 October 2024.Results showed that the spatial distributions of CO_(2) emis-sions within a 3 km radius were mapped,and the local diffusion processes were clarified.CO_(2) patterns varied differently in two industrial parks over the three-month period:Average CO_(2) concentrations in non-emission areas were 422.4 ppm in Pucheng and 408.7 ppm in Nanping,with the former experiencing higher and more variable carbon emissions;Correlation analysis indicated that synthetic leather factories in Pucheng contributed more to SO_(2) and NO_(x) levels compared to the chemical plant in Nanping;In Pucheng,CO_(2) concentrations were transported from the north at ground-level wind speeds exceeding 4 m/s,while in Nanping,the concentrations dispersed gradually with increasing wind speeds;Forward trajectory simulations revealed that the peak-emission from Pucheng primarily affected southern Fujian,northeastern Jiangxi,and southern Anhui,while the peak-emission from Nanping influenced central and western Fujian and northeastern Jiangxi.Besides,emissions in both industrial parks were higher on weekdays and lower on weekends,reflecting changes in industrial activi-ties.The study underscores the potential of lidar technology for providing detailed insights into CO_(2) distribution and the interactions between emissions,wind patterns,and carbon transport.
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.
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。
