Precise vehicle ego-localization using feature matching of pavement images  

作　　者：Zijun Jiang Zhigang Xu Yunchao Li Haigen Min Jingmei Zhou 

机构地区：[1]Chang’an University,Xi’an,China [2]School of Information Engineering,Chang’an University,Xi’an,China

出　　处：《Journal of Intelligent and Connected Vehicles》2020年第2期37-47,共11页智能网联汽车（英文）

摘　　要：Purpose–Precise vehicle localization is a basic and critical technique for various intelligent transportation system(ITS)applications.It also needs to adapt to the complex road environments in real-time.The global positioning system and the strap-down inertial navigation system are two common techniques in thefield of vehicle localization.However,the localization accuracy,reliability and real-time performance of these two techniques can not satisfy the requirement of some critical ITS applications such as collision avoiding,vision enhancement and automatic parking.Aiming at the problems above,this paper aims to propose a precise vehicle ego-localization method based on image matching.Design/methodology/approach–This study included three steps,Step 1,extraction of feature points.After getting the image,the local features in the pavement images were extracted using an improved speeded up robust features algorithm.Step 2,eliminate mismatch points.Using a random sample consensus algorithm to eliminate mismatched points of road image and make match point pairs more robust.Step 3,matching of feature points and trajectory generation.Findings–Through the matching and validation of the extracted local feature points,the relative translation and rotation offsets between two consecutive pavement images were calculated,eventually,the trajectory of the vehicle was generated.Originality/value–The experimental results show that the studied algorithm has an accuracy at decimeter-level and it fully meets the demand of the lane-level positioning in some critical ITS applications.

关 键 词：Feature extraction Image matching Intelligent transportation systems Intelligent vehicles Position measurement 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]