摘要
农业劳动力短缺与成本高涨问题日益凸显,研发农机无人驾驶技术势在必行。该研究针对无人驾驶农机对高精度农田地图的需要,提出一种高精度农田地图构建方法。将地图分为地块信息层、障碍物层、作业信息层和动态感知层,并定义前两层的地图模型。针对地块信息管理,采用地块边界层为数据管理单元,定义了边界层的拓扑关系与限制信息。针对障碍物管理,并定义了不同几何属性的障碍物,分别表达形状、类型及语义等信息。同时提出了一种基于多旋翼无人机和Autoware的地图数据采集、标注与发布方法。在北京市密云区开展了地图构建试验,布设了12个检验点用于地图精度评价。结果表明,本研究制作的高精度地图的绝对定位精度优于0.1 m,平面误差的标准差小于2 cm,因建图产生的地图拉伸与压缩误差在厘米级以内。所提出的农田高精度地图架构可满足无人驾驶农机作业对地图精度的需求,可为农机作业路径规划和障碍物感知提供先验信息,降低无人驾驶应用对单机智能化的要求。
The problem of agricultural labor shortage and high cost has become increasingly prominent,and the research and development of unmanned agricultural machinery technology are imperative.In order to meet the needs of unmanned agricultural machinery for high-precision farmland maps,a method of high-precision farmland map construction was proposed.Since the obstacles,facilities,and boundaries were complex infield,it was different to construct the high-precision map in the field than on the road.The main work of this paper includes five tasks:1)defining different map layers by agricultural features;2)collecting the field photograph via UAV in Heinanzhai Town,Miyun District,Beijing;3)constructing the dense point cloud and 3D model through aerial slice stitching and processing;4)annotating the dense point cloud data with different layer types to construct final maps;5)The feature recognition,semantic definition and map annotation are carried out based on Autoware.For the different levels of unmanned agricultural machines and various scenarios needs,the high precision maps should be designed and implemented in layers,a five-layer high precision map construction was proposed,which included a field boundary layer,static obstacle layer,operational information layer,dynamic perception layer,and brain-like layer.The field boundary layer and static obstacle layer which can satisfy the demand for the tracing operation in a closed environment was built.For the field boundary layer,the boundary lines,the entrance and exit data structure,and the corresponding topological relationships were considered.For the obstacle layer,shapes such as curves,polygons,circles,and rectangles were used to describe various obstacles with different geometric properties.The tests were conducted in clear weather,and 511 images were obtained from the aerial survey.All the aerials were positioned in a fixed position.12 target inspection points were evenly arranged in the field,entrance,exit,and roadside before the flight,and the precise location was obt
作者
赵欣
王万里
董靓
徐媛媛
王科
翟卫欣
吴才聪
Zhao Xin;Wang Wanli;Dong Liang;Xu Yuanyuan;Wang Ke;Zhai Weixin;Wu Caicong(College of Information and Electrical Engineering,China Agricultural University,Beijing 100083,China;Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications,Ministry of Agriculture and Rural Affairs,Beijing 100083,China)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2022年第S01期1-7,共7页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家重点研发计划项目(2021YFB3901302)
北京市科技计划项目(Z201100008020008)。
