Open AccessReal‐Time Precision Vehicle Localization Using Numerical Maps
Author(s) -
Han SeungJun,
Choi Jeongdan
Publication year - 2014
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.14.0114.0040
Subject(s) - kalman filter , automotive industry , particle filter , field (mathematics) , software , feature (linguistics) , computer science , advanced driver assistance systems , extended kalman filter , engineering , computer vision , artificial intelligence , real time computing , aerospace engineering , linguistics , philosophy , mathematics , pure mathematics , programming language
Autonomous vehicle technology based on information technology and software will lead the automotive industry in the near future. Vehicle localization technology is a core expertise geared toward developing autonomous vehicles and will provide location information for control and decision. This paper proposes an effective vision‐based localization technology to be applied to autonomous vehicles. In particular, the proposed technology makes use of numerical maps that are widely used in the field of geographic information systems and that have already been built in advance. Optimum vehicle ego‐motion estimation and road marking feature extraction techniques are adopted and then combined by an extended Kalman filter and particle filter to make up the localization technology. The implementation results of this paper show remarkable results; namely, an 18 ms mean processing time and 10 cm location error. In addition, autonomous driving and parking are successfully completed with an unmanned vehicle within a 300 m × 500 m space.