Background:Individual tree extraction from terrestrial laser scanning(TLS)data is a prerequisite for tree-scale estimations of forest biophysical properties.This task currently is undertaken through laborious and time...Background:Individual tree extraction from terrestrial laser scanning(TLS)data is a prerequisite for tree-scale estimations of forest biophysical properties.This task currently is undertaken through laborious and time-consuming manual assistance and quality control.This study presents a new fully automatic approach to extract single trees from large-area TLS data.This data-driven method operates exclusively on a point cloud graph by path finding,which makes our method computationally efficient and universally applicable to data from various forest types.Results:We demonstrated the proposed method on two openly available datasets.First,we achieved state-of-the-art performance on locating single trees on a benchmark dataset by significantly improving the mean accuracy by over 10% especially for difficult forest plots.Second,we successfully extracted 270 trees from one hectare temperate forest.Quantitative validation resulted in a mean Intersection over Union(mIoU)of 0.82 for single crown segmentation,which further led to a relative root mean square error(RMSE%)of 21.2% and 23.5% for crown area and tree volume estimations,respectively.Conclusions:Our method allows automated access to individual tree level information from TLS point clouds.The proposed method is free from restricted assumptions of forest types.It is also computationally efficient with an average processing time of several seconds for one million points.It is expected and hoped that our method would contribute to TLS-enabled wide-area forest qualifications,ranging from stand volume and carbon stocks modelling to derivation of tree functional traits as part of the global ecosystem understanding.展开更多
This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapi...This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapidly-exploring Random Trees*(Q-RRT*)algorithm.A cost inequality relationship between an ancestor and its descendants was derived,and the ancestors were filtered accordingly.Secondly,the underwater gravity-aided navigation path planning system was designed based on the DSFS algorithm,taking into account the fitness,safety,and asymptotic optimality of the routes,according to the gravity suitability distribution of the navigation space.Finally,experimental comparisons of the computing performance of the ChooseParent procedure,the Rewire procedure,and the combination of the two procedures for Q-RRT*and DSFS were conducted under the same planning environment and parameter conditions,respectively.The results showed that the computational efficiency of the DSFS algorithm was improved by about 1.2 times compared with the Q-RRT*algorithm while ensuring correct computational results.展开更多
Terrestrial laser scanning(TLS)accurately captures tree structural information and provides prerequisites for treescale estimations of forest biophysical attributes.Quantifying tree-scale attributes from TLS point clo...Terrestrial laser scanning(TLS)accurately captures tree structural information and provides prerequisites for treescale estimations of forest biophysical attributes.Quantifying tree-scale attributes from TLS point clouds requires segmentation,yet the occlusion effects severely affect the accuracy of automated individual tree segmentation.In this study,we proposed a novel method using ellipsoid directional searching and point compensation algorithms to alleviate occlusion effects.Firstly,region growing and point compensation algorithms are used to determine the location of tree roots.Secondly,the neighbor points are extracted within an ellipsoid neighborhood to mitigate occlusion effects compared with k-nearest neighbor(KNN).Thirdly,neighbor points are uniformly subsampled by the directional searching algorithm based on the Fibonacci principle in multiple spatial directions to reduce memory consumption.Finally,a graph describing connectivity between a point and its neighbors is constructed,and it is utilized to complete individual tree segmentation based on the shortest path algorithm.The proposed method was evaluated on a public TLS dataset comprising six forest plots with three complexity categories in Evo,Finland,and it reached the highest mean accuracy of 77.5%,higher than previous studies on tree detection.We also extracted and validated the tree structure attributes using manual segmentation reference values.The RMSE,RMSE%,bias,and bias%of tree height,crown base height,crown projection area,crown surface area,and crown volume were used to evaluate the segmentation accuracy,respectively.Overall,the proposed method avoids many inherent limitations of current methods and can accurately map canopy structures in occluded complex forest stands.展开更多
计算能力弱、存储容量小是普通物联网节点的典型特征,复杂的部署环境和不稳定的无线链路又会导致物联网网络状态频繁变化.所以,物联网中固定的传输路径无法提供高效的感知及数据传输服务.例如典型的树型路由结构中,靠近树根的节点要提...计算能力弱、存储容量小是普通物联网节点的典型特征,复杂的部署环境和不稳定的无线链路又会导致物联网网络状态频繁变化.所以,物联网中固定的传输路径无法提供高效的感知及数据传输服务.例如典型的树型路由结构中,靠近树根的节点要提供的传输任务较重,能量消耗更快,会导致整个网络部署周期变短.本文提出了一种路径可实时定义的物联网传输模型(IoT Transmission Model with Real-time Path Definition,ITRP),物联子网中所有节点将邻接关系上报给网关设备,由性能占优的有源供电网关设备来定义网络的实时路由树.网关向物联子网节点发送报文时会携带转发标签,后续转发节点只需根据标签完成报文传输,并根据上一跳信息建立其到网关的反向传输路径.ITRP模型可围绕特定的网络服务目标(节能、传输安全、带宽保障等)收集相关网络状态信息,并周期性调整路由拓扑,实现物联网传输服务的优化.实验面向能量均衡目标展开,经过10个信息采集周期,ITRP模型相对确定性路由模型能量最低节点的能耗比为44%~86%,相对自适应多径传输模型能量最低节点的能耗比为63%~86%;而且,ITRP模型只需较小的标签代价,实验环境中报文的平均标签长度不超过5比特.展开更多
面对采摘作业的复杂环境,提出了一种终点区域RRT(Goal Area RRT,GA-RRT)算法,以提高路径生成的效率并降低路径成本。根据环境系数确定初始步长与终点区域,当拓展节点进入终点区域后,随机点生成范围缩小至终点区域,同时调整步长;然后,在...面对采摘作业的复杂环境,提出了一种终点区域RRT(Goal Area RRT,GA-RRT)算法,以提高路径生成的效率并降低路径成本。根据环境系数确定初始步长与终点区域,当拓展节点进入终点区域后,随机点生成范围缩小至终点区域,同时调整步长;然后,在此基础上引入目标概率偏向方法,提高路径搜索效率;最后,对生成的路径进行简化节点处理以减少路径代价,并使用三次B样条方法平滑路径。仿真实验结果表明:二维环境下,GA-RRT算法相较于RRT、RRT-Connect算法,耗时缩短85.15%、29.86%,路径代价减少19.18%、18.26%;机械臂仿真环境下,与引入目标概率偏向方法的RRT算法进行比较,耗时缩短54.70%,路径代价减少51.59°。利用IRB120机械臂实验平台,验证了算法的可行性。展开更多
基金partially funded by the Scientific Research Foundation of Xidian Universitypart of 3DForMod project(ANR-17-EGAS-0002-01)funded in the frame of the JPI FACCE ERA-GAS call funded under European Union’s Horizon 2020 research and innovation program(grant agreement No.696356).
文摘Background:Individual tree extraction from terrestrial laser scanning(TLS)data is a prerequisite for tree-scale estimations of forest biophysical properties.This task currently is undertaken through laborious and time-consuming manual assistance and quality control.This study presents a new fully automatic approach to extract single trees from large-area TLS data.This data-driven method operates exclusively on a point cloud graph by path finding,which makes our method computationally efficient and universally applicable to data from various forest types.Results:We demonstrated the proposed method on two openly available datasets.First,we achieved state-of-the-art performance on locating single trees on a benchmark dataset by significantly improving the mean accuracy by over 10% especially for difficult forest plots.Second,we successfully extracted 270 trees from one hectare temperate forest.Quantitative validation resulted in a mean Intersection over Union(mIoU)of 0.82 for single crown segmentation,which further led to a relative root mean square error(RMSE%)of 21.2% and 23.5% for crown area and tree volume estimations,respectively.Conclusions:Our method allows automated access to individual tree level information from TLS point clouds.The proposed method is free from restricted assumptions of forest types.It is also computationally efficient with an average processing time of several seconds for one million points.It is expected and hoped that our method would contribute to TLS-enabled wide-area forest qualifications,ranging from stand volume and carbon stocks modelling to derivation of tree functional traits as part of the global ecosystem understanding.
基金the National Natural Science Foundation of China(Grant No.42274119)the Liaoning Revitalization Talents Program(Grant No.XLYC2002082)+1 种基金National Key Research and Development Plan Key Special Projects of Science and Technology Military Civil Integration(Grant No.2022YFF1400500)the Key Project of Science and Technology Commission of the Central Military Commission.
文摘This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapidly-exploring Random Trees*(Q-RRT*)algorithm.A cost inequality relationship between an ancestor and its descendants was derived,and the ancestors were filtered accordingly.Secondly,the underwater gravity-aided navigation path planning system was designed based on the DSFS algorithm,taking into account the fitness,safety,and asymptotic optimality of the routes,according to the gravity suitability distribution of the navigation space.Finally,experimental comparisons of the computing performance of the ChooseParent procedure,the Rewire procedure,and the combination of the two procedures for Q-RRT*and DSFS were conducted under the same planning environment and parameter conditions,respectively.The results showed that the computational efficiency of the DSFS algorithm was improved by about 1.2 times compared with the Q-RRT*algorithm while ensuring correct computational results.
基金supported by the National Natural Science Foundation of China(Nos.32171789,32211530031,12411530088)the National Key Research and Development Program of China(No.2023YFF1303901)+2 种基金the Joint Open Funded Project of State Key Laboratory of Geo-Information Engineering and Key Laboratory of the Ministry of Natural Resources for Surveying and Mapping Science and Geo-spatial Information Technology(2022-02-02)Background Resources Survey in Shennongjia National Park(SNJNP2022001)the Open Project Fund of Hubei Provincial Key Laboratory for Conservation Biology of Shennongjia Snub-nosed Monkeys(SNJGKL2022001).
文摘Terrestrial laser scanning(TLS)accurately captures tree structural information and provides prerequisites for treescale estimations of forest biophysical attributes.Quantifying tree-scale attributes from TLS point clouds requires segmentation,yet the occlusion effects severely affect the accuracy of automated individual tree segmentation.In this study,we proposed a novel method using ellipsoid directional searching and point compensation algorithms to alleviate occlusion effects.Firstly,region growing and point compensation algorithms are used to determine the location of tree roots.Secondly,the neighbor points are extracted within an ellipsoid neighborhood to mitigate occlusion effects compared with k-nearest neighbor(KNN).Thirdly,neighbor points are uniformly subsampled by the directional searching algorithm based on the Fibonacci principle in multiple spatial directions to reduce memory consumption.Finally,a graph describing connectivity between a point and its neighbors is constructed,and it is utilized to complete individual tree segmentation based on the shortest path algorithm.The proposed method was evaluated on a public TLS dataset comprising six forest plots with three complexity categories in Evo,Finland,and it reached the highest mean accuracy of 77.5%,higher than previous studies on tree detection.We also extracted and validated the tree structure attributes using manual segmentation reference values.The RMSE,RMSE%,bias,and bias%of tree height,crown base height,crown projection area,crown surface area,and crown volume were used to evaluate the segmentation accuracy,respectively.Overall,the proposed method avoids many inherent limitations of current methods and can accurately map canopy structures in occluded complex forest stands.
文摘计算能力弱、存储容量小是普通物联网节点的典型特征,复杂的部署环境和不稳定的无线链路又会导致物联网网络状态频繁变化.所以,物联网中固定的传输路径无法提供高效的感知及数据传输服务.例如典型的树型路由结构中,靠近树根的节点要提供的传输任务较重,能量消耗更快,会导致整个网络部署周期变短.本文提出了一种路径可实时定义的物联网传输模型(IoT Transmission Model with Real-time Path Definition,ITRP),物联子网中所有节点将邻接关系上报给网关设备,由性能占优的有源供电网关设备来定义网络的实时路由树.网关向物联子网节点发送报文时会携带转发标签,后续转发节点只需根据标签完成报文传输,并根据上一跳信息建立其到网关的反向传输路径.ITRP模型可围绕特定的网络服务目标(节能、传输安全、带宽保障等)收集相关网络状态信息,并周期性调整路由拓扑,实现物联网传输服务的优化.实验面向能量均衡目标展开,经过10个信息采集周期,ITRP模型相对确定性路由模型能量最低节点的能耗比为44%~86%,相对自适应多径传输模型能量最低节点的能耗比为63%~86%;而且,ITRP模型只需较小的标签代价,实验环境中报文的平均标签长度不超过5比特.
文摘面对采摘作业的复杂环境,提出了一种终点区域RRT(Goal Area RRT,GA-RRT)算法,以提高路径生成的效率并降低路径成本。根据环境系数确定初始步长与终点区域,当拓展节点进入终点区域后,随机点生成范围缩小至终点区域,同时调整步长;然后,在此基础上引入目标概率偏向方法,提高路径搜索效率;最后,对生成的路径进行简化节点处理以减少路径代价,并使用三次B样条方法平滑路径。仿真实验结果表明:二维环境下,GA-RRT算法相较于RRT、RRT-Connect算法,耗时缩短85.15%、29.86%,路径代价减少19.18%、18.26%;机械臂仿真环境下,与引入目标概率偏向方法的RRT算法进行比较,耗时缩短54.70%,路径代价减少51.59°。利用IRB120机械臂实验平台,验证了算法的可行性。
