Swarm robot systems are an important application of autonomous unmanned surface vehicles on water surfaces.For monitoring natural environments and conducting security activities within a certain range using a surface ...Swarm robot systems are an important application of autonomous unmanned surface vehicles on water surfaces.For monitoring natural environments and conducting security activities within a certain range using a surface vehicle,the swarm robot system is more efficient than the operation of a single object as the former can reduce cost and save time.It is necessary to detect adjacent surface obstacles robustly to operate a cluster of unmanned surface vehicles.For this purpose,a LiDAR(light detection and ranging)sensor is used as it can simultaneously obtain 3D information for all directions,relatively robustly and accurately,irrespective of the surrounding environmental conditions.Although the GPS(global-positioning-system)error range exists,obtaining measurements of the surface-vessel position can still ensure stability during platoon maneuvering.In this study,a three-layer convolutional neural network is applied to classify types of surface vehicles.The aim of this approach is to redefine the sparse 3D point cloud data as 2D image data with a connotative meaning and subsequently utilize this transformed data for object classification purposes.Hence,we have proposed a descriptor that converts the 3D point cloud data into 2D image data.To use this descriptor effectively,it is necessary to perform a clustering operation that separates the point clouds for each object.We developed voxel-based clustering for the point cloud clustering.Furthermore,using the descriptor,3D point cloud data can be converted into a 2D feature image,and the converted 2D image is provided as an input value to the network.We intend to verify the validity of the proposed 3D point cloud feature descriptor by using experimental data in the simulator.Furthermore,we explore the feasibility of real-time object classification within this framework.展开更多
The influence of laser beam divergence angle on the positioning accuracy of scanning airborne light detection and ranging （LIDAR） is analyzed and simulated. Based on the data process and positioning principle of air...The influence of laser beam divergence angle on the positioning accuracy of scanning airborne light detection and ranging （LIDAR） is analyzed and simulated. Based on the data process and positioning principle of airborne LIDAR, the errors from pulse broadening induced by laser beam di vergence angle are modeled and qualitatively analyzed for different terrain surfaces. Simulated results of positioning errors and suggestions to reduce them are given for the flat surface, the downhill of slope surface, and the uphill surface.展开更多
Numerous studies have been performed to better understand the behavior of wake vortices with regards to aircraft characteristics and weather conditionsover the pastten years. These studies have led to the development ...Numerous studies have been performed to better understand the behavior of wake vortices with regards to aircraft characteristics and weather conditionsover the pastten years. These studies have led to the development of the aircraft RECATegorization(RECAT) programs in Europe and in USA. Its phase one focused on redefining distance separation matrix with six static aircraft wake turbulence categories instead of three with the current International Civil Aviation Organization(ICAO) regulations. In Europe, the RECAT-EU regulation is now entering under operational implementation atseveral key airports. As proven by several research projects in the past, LIght Detection And Ranging(LIDAR) sensors are considered as the ground truth wake vortex measurements for assessing the safety impact of a new wake turbulence regulation at an airport in quantifying the risks given the local specificities. LIDAR's can also be used to perform risk monitoring after the implementation. In this paper, the principle to measure wake vortices with scanning coherent Doppler LIDARs is described as well as its dedicated post-processing. Finally the use of WINDCUBELIDAR based solution for supporting the implementation of new wake turbulenceregulation is described along with satisfyingresults that have permitted the monitoring of the wake vortex encounter risk after the implementation of a new wake turbulence regulation.展开更多
针对当前高速公路建设工期短、建设环境复杂的特点,介绍了机载LiDAR(Light detection and ranging,激光雷达)技术及其在高速公路勘测中的应用情况。实践表明,机载LiDAR能够快速为高速公路勘察设计提供高精度的数字化地理产品,具有良好...针对当前高速公路建设工期短、建设环境复杂的特点,介绍了机载LiDAR(Light detection and ranging,激光雷达)技术及其在高速公路勘测中的应用情况。实践表明,机载LiDAR能够快速为高速公路勘察设计提供高精度的数字化地理产品,具有良好的应用前景。展开更多
With the development of sensors,the application of multi-source remote sensing data has been widely concerned.Since hyperspectral image(HSI)contains rich spectral information while light detection and ranging(LiDAR)da...With the development of sensors,the application of multi-source remote sensing data has been widely concerned.Since hyperspectral image(HSI)contains rich spectral information while light detection and ranging(LiDAR)data contains elevation information,joint use of them for ground object classification can yield positive results,especially by building deep networks.Fortu-nately,multi-scale deep networks allow to expand the receptive fields of convolution without causing the computational and training problems associated with simply adding more network layers.In this work,a multi-scale feature fusion network is proposed for the joint classification of HSI and LiDAR data.First,we design a multi-scale spatial feature extraction module with cross-channel connections,by which spatial information of HSI data and elevation information of LiDAR data are extracted and fused.In addition,a multi-scale spectral feature extraction module is employed to extract the multi-scale spectral features of HSI data.Finally,joint multi-scale features are obtained by weighting and concatenation operations and then fed into the classifier.To verify the effective-ness of the proposed network,experiments are carried out on the MUUFL Gulfport and Trento datasets.The experimental results demonstrate that the classification performance of the proposed method is superior to that of other state-of-the-art methods.展开更多
基金supported by the Future Challenge Program through the Agency for Defense Development funded by the Defense Acquisition Program Administration (No.UC200015RD)。
文摘Swarm robot systems are an important application of autonomous unmanned surface vehicles on water surfaces.For monitoring natural environments and conducting security activities within a certain range using a surface vehicle,the swarm robot system is more efficient than the operation of a single object as the former can reduce cost and save time.It is necessary to detect adjacent surface obstacles robustly to operate a cluster of unmanned surface vehicles.For this purpose,a LiDAR(light detection and ranging)sensor is used as it can simultaneously obtain 3D information for all directions,relatively robustly and accurately,irrespective of the surrounding environmental conditions.Although the GPS(global-positioning-system)error range exists,obtaining measurements of the surface-vessel position can still ensure stability during platoon maneuvering.In this study,a three-layer convolutional neural network is applied to classify types of surface vehicles.The aim of this approach is to redefine the sparse 3D point cloud data as 2D image data with a connotative meaning and subsequently utilize this transformed data for object classification purposes.Hence,we have proposed a descriptor that converts the 3D point cloud data into 2D image data.To use this descriptor effectively,it is necessary to perform a clustering operation that separates the point clouds for each object.We developed voxel-based clustering for the point cloud clustering.Furthermore,using the descriptor,3D point cloud data can be converted into a 2D feature image,and the converted 2D image is provided as an input value to the network.We intend to verify the validity of the proposed 3D point cloud feature descriptor by using experimental data in the simulator.Furthermore,we explore the feasibility of real-time object classification within this framework.
基金Supported by the National Basic Research Program of China("973"Program)(2009CB72400401A)
文摘The influence of laser beam divergence angle on the positioning accuracy of scanning airborne light detection and ranging （LIDAR） is analyzed and simulated. Based on the data process and positioning principle of airborne LIDAR, the errors from pulse broadening induced by laser beam di vergence angle are modeled and qualitatively analyzed for different terrain surfaces. Simulated results of positioning errors and suggestions to reduce them are given for the flat surface, the downhill of slope surface, and the uphill surface.
文摘Numerous studies have been performed to better understand the behavior of wake vortices with regards to aircraft characteristics and weather conditionsover the pastten years. These studies have led to the development of the aircraft RECATegorization(RECAT) programs in Europe and in USA. Its phase one focused on redefining distance separation matrix with six static aircraft wake turbulence categories instead of three with the current International Civil Aviation Organization(ICAO) regulations. In Europe, the RECAT-EU regulation is now entering under operational implementation atseveral key airports. As proven by several research projects in the past, LIght Detection And Ranging(LIDAR) sensors are considered as the ground truth wake vortex measurements for assessing the safety impact of a new wake turbulence regulation at an airport in quantifying the risks given the local specificities. LIDAR's can also be used to perform risk monitoring after the implementation. In this paper, the principle to measure wake vortices with scanning coherent Doppler LIDARs is described as well as its dedicated post-processing. Finally the use of WINDCUBELIDAR based solution for supporting the implementation of new wake turbulenceregulation is described along with satisfyingresults that have permitted the monitoring of the wake vortex encounter risk after the implementation of a new wake turbulence regulation.
基金supported by the National Key Research and Development Project(No.2020YFC1512000)the General Projects of Key R&D Programs in Shaanxi Province(No.2020GY-060)Xi’an Science&Technology Project(No.2020KJRC 0126)。
文摘With the development of sensors,the application of multi-source remote sensing data has been widely concerned.Since hyperspectral image(HSI)contains rich spectral information while light detection and ranging(LiDAR)data contains elevation information,joint use of them for ground object classification can yield positive results,especially by building deep networks.Fortu-nately,multi-scale deep networks allow to expand the receptive fields of convolution without causing the computational and training problems associated with simply adding more network layers.In this work,a multi-scale feature fusion network is proposed for the joint classification of HSI and LiDAR data.First,we design a multi-scale spatial feature extraction module with cross-channel connections,by which spatial information of HSI data and elevation information of LiDAR data are extracted and fused.In addition,a multi-scale spectral feature extraction module is employed to extract the multi-scale spectral features of HSI data.Finally,joint multi-scale features are obtained by weighting and concatenation operations and then fed into the classifier.To verify the effective-ness of the proposed network,experiments are carried out on the MUUFL Gulfport and Trento datasets.The experimental results demonstrate that the classification performance of the proposed method is superior to that of other state-of-the-art methods.
