典型文献
A Lane-Level Road Marking Map Using a Monocular Camera
文献摘要：
The essential requirement for precise localization of a self-driving car is a lane-level map which includes road markings (RMs). Obviously, we can build the lane-level map by running a mobile mapping system (MMS) which is equipped with a high-end 3D LiDAR and a number of high-cost sensors. This approach, however, is highly expensive and ineffective since a single high-end MMS must visit every place for mapping. In this paper, a lane-level RM mapping system using a monocular camera is developed. The developed system can be considered as an alternative to expensive high-end MMS. The developed RM map includes the information of road lanes (RLs) and symbolic road markings (SRMs). First, to build a lane-level RM map, the RMs are segmented at pixel level through the deep learning network. The network is named RMNet. The segmented RMs are then gathered to build a lane-level RM map. Second, the lane-level map is improved through loop-closure detection and graph optimization. To train the RMNet and build a lane-level RM map, a new dataset named SeRM set is developed. The set is a large dataset for lane-level RM mapping and it includes a total of 25157 pixel-wise annotated images and 21000 position labeled images. Finally, the proposed lane-level map building method is applied to SeRM set and its validity is demonstrated through experimentation.
文献关键词：
中图分类号：
作者姓名：
Wonje Jang
作者机构：
School of Electrical and Electronic Engineering,Yonsei University,Seoul 120-749,Korea
文献出处：
引用格式：
[1]Wonje Jang-.A Lane-Level Road Marking Map Using a Monocular Camera)[J].自动化学报（英文版）,2022(01):187-204
A类：
RLs,SRMs,RMNet,SeRM
B类：
Lane,Level,Road,Marking,Map,Using,Monocular,Camera,essential,requirement,precise,localization,self,driving,car,level,which,includes,road,markings,Obviously,can,by,running,mobile,mapping,system,MMS,equipped,end,LiDAR,number,cost,sensors,This,approach,however,highly,expensive,ineffective,since,single,must,visit,every,place,In,this,paper,using,monocular,camera,developed,considered,alternative,information,lanes,symbolic,First,are,segmented,pixel,through,deep,learning,network,named,then,gathered,Second,improved,loop,closure,detection,graph,optimization,To,train,new,dataset,large,total,wise,annotated,images,position,labeled,Finally,proposed,building,method,applied,its,validity,demonstrated,experimentation
AB值：
0.457443
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