首页|Development of vehicle-recognition method on water surfaces using LiDAR data:SPD2(spherically stratified point projection with diameter and distance)
Development of vehicle-recognition method on water surfaces using LiDAR data:SPD2(spherically stratified point projection with diameter and distance)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
万方数据
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-posi-tioning-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 oper-ation 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.
Object classificationClustering3D point cloud dataLiDAR(light detection and ranging)Surface vehicle
Eon-ho Lee、Hyeon Jun Jeon、Jinwoo Choi、Hyun-Taek Choi、Sejin Lee
展开 >
The Department of Mechanical Engineering,Kongju National University,1223-24 Cheonan-daero,Cheonan 31080,Republic of Korea
Ocean System Engineering Research Division,Korea Research Institute of Ships and Ocean Engineering(KRISO),Daejeon 34103,Republic of Korea
The Division of Mechanical & Automotive Engineering,Kongju National University,1223-24 Cheonan-daero,Cheonan 31080,Republic of Korea
Future Challenge Program through the Agency for Defense Development funded by the Defense Acquisition Program Administration
NETL
NSTL
万方数据
