Vehicle positioning is critical for inter-vehicle communication, navigation, vehicle monitoring and
tracking. They are regarded as the core technology ensuring safety in everyday-driving. This paper proposes an
enhanced vehicle ego-localization method based on streetscape image database. It is most useful in the global
positioning system (GPS) blind area. Firstly, a database is built by collecting streetscape images, extracting
dominant color feature and detecting speeded up robust feature (SURF) points. Secondly, an image that the
vehicle shoots at one point is analyzed to find a matching image in the database by dynamic programming (DP)
matching. According to the image similarity, several images with higher probabilities are selected to realize coarse
positioning. Finally, different weights are set to the coordinates of the shooting location with the maximum similarity
and its 8 neighborhoods according to the number of matching points, and then interpolating calculation
is applied to complete accurate positioning. Experimental results show that the accuracy of this study is less
than 1.5m and its running time is about 3.6 s. These are basically in line with the practical need. The described
system has an advantage of low cost, high reliability and strong resistance to signal interference, so it has a better
practical value as compared with visual odometry (VO) and radio frequency identification (RFID) based approach
for vehicle positioning in the case of GPS not working.
ZHOU Jingmei *(周经美), ZHAO Xiangmo (赵祥模), CHENG Xin (程鑫), XU Zhigang (徐志刚), ZHAO Huaixin (赵怀鑫)
. Vehicle Ego-Localization Based on Streetscape Image Database Under Blind Area of Global Positioning System[J]. Journal of Shanghai Jiaotong University(Science), 2019
, 24(1)
: 122
-129
.
DOI: 10.1007/s12204-018-2008-8
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