Vehicle Ego-Localization Based on Streetscape Image Database Under Blind Area of Global Positioning System
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摘要
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 1.5 m 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.
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 1.5 m 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.
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 1.5 m 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.
引文
[1] TARD′OS J D, NEIRA J, NEWMAN P M, et al. Robust mapping and localization in indoor environments using sonar data[J]. International Journal of Robotics Research, 2002, 21(4):311-330.
[2] SHIBUHISA N, SATO J, TAKAHASHI T, et al.Accurate vehicle localization using DTW between range data map and laser scanner data sequences[C]//Proceedings of the 2007 IEEE Intelligent Vehicles Symposium. Istanbul, Turkey:IEEE, 2007:975-980.
[3] AGRAWAL M, KONOLIGE K. Real-time localization in outdoor environments using stereo vision and inexpensive GPS[C]//Proceedings of the 18th International Conference on Pattern Recognition. Hong Kong,China:IEEE, 2006:1063-1068.
[4] MATSUMOTO Y, SAKAI K, INABA M, et al. Viewbased approach to robot navigation[J]. Journal of the Robotics Society of Japan, 2000, 20(5):1702-1708.
[5] ZHANG E Z, KUANG Y J, JIANG W L, et al.Active RFID positioning of vehicles in road traffic[C]//Proceedings of the 11th International Symposium on Communications and Information Technologies. Hangzhou, China:IEEE, 2011:222-227.
[6] HUANG J F, LI X, SUN Y H, et al. A highly-reliable combined positioning method for vehicle in urban complex environments[C]//Proceedings of the International Conference on Vehicular Electronics and Safety.Dongguan, China:IEEE, 2013:153-158.
[7] BOUCHER C, NOYER J C. A hybrid particle approach for GNSS applications with partial GPS outages[J]. IEEE Transactions on Instrumentation and Measurement, 2010, 59(3):498-505.
[8] NIST′ER D, NARODITSKY O, BERGEN J. Visual odometry for ground vehicle applications[J]. Journal of Field Robotics, 2010, 23(1):3-20.
[9] FARENZENA M, BARTOLI A, MEZOUAR Y.Efficient camera smoothing in sequential structurefrom-motion using approximate cross-validation[C]//European Conference on Computer Vision.Marseille, France:Springer-Verlag, 2008:196-209.
[10] ZHOU J M, ZHAO X M, XU Z G, et al. Algorithm of vehicle trajectory extraction using fusion of Hu moments and grayscale feature[J]. Information Technology Journal, 2014, 13(11):1770-1778.
[11] SATO J, TAKAHASHI T, IDE I, et al. Change detection in streetscapes from GPS coordinated omnidirectional image sequences[C]//Proceedings of the18th International Conference on Pattern Recognition.Hong Kong, China:IEEE, 2006:935-938.
[12] UCHIYAMA H, TAKAHASHI T, IDE I, et al.Frame registration of in-vehicle normal camera with omni-directional camera for self-position estimation[C]//International Conference on Innovative Computing Information and Control. Dalian, China:IEEE,2008:7.
[13] UCHIYAMA H, DEGUCHI D, TAKAHASHI T, et al. Ego-localization using streetscape image sequences from in-vehicle cameras[C]//Intelligent Vehicles Symposium. Xi’an, China:IEEE, 2009:185-190.
[14] ZHOU J M, ZHAO X M, WANG Z, et al. An automatic calibration system of vehicle motion error under GPS blind area[C]//International Conference on Intelligent Transportation Systems. Las Palmas, Spain:IEEE, 2015:238-243.
[15] SUN J. Research on content-based image retrieval[D].Xi’an, China:School of Computer Science, Xidian University, 2005(in Chinese).
[16] LIU Y, EMOTO H, FUJII T, et al. A method for content-based similarity retrieval of images using two dimensional DP matching algorithm[C]//International Conference on Image Analysis and Processing. Palermo, Italy:IEEE, 2001:236-241.
[2] SHIBUHISA N, SATO J, TAKAHASHI T, et al.Accurate vehicle localization using DTW between range data map and laser scanner data sequences[C]//Proceedings of the 2007 IEEE Intelligent Vehicles Symposium. Istanbul, Turkey:IEEE, 2007:975-980.
[3] AGRAWAL M, KONOLIGE K. Real-time localization in outdoor environments using stereo vision and inexpensive GPS[C]//Proceedings of the 18th International Conference on Pattern Recognition. Hong Kong,China:IEEE, 2006:1063-1068.
[4] MATSUMOTO Y, SAKAI K, INABA M, et al. Viewbased approach to robot navigation[J]. Journal of the Robotics Society of Japan, 2000, 20(5):1702-1708.
[5] ZHANG E Z, KUANG Y J, JIANG W L, et al.Active RFID positioning of vehicles in road traffic[C]//Proceedings of the 11th International Symposium on Communications and Information Technologies. Hangzhou, China:IEEE, 2011:222-227.
[6] HUANG J F, LI X, SUN Y H, et al. A highly-reliable combined positioning method for vehicle in urban complex environments[C]//Proceedings of the International Conference on Vehicular Electronics and Safety.Dongguan, China:IEEE, 2013:153-158.
[7] BOUCHER C, NOYER J C. A hybrid particle approach for GNSS applications with partial GPS outages[J]. IEEE Transactions on Instrumentation and Measurement, 2010, 59(3):498-505.
[8] NIST′ER D, NARODITSKY O, BERGEN J. Visual odometry for ground vehicle applications[J]. Journal of Field Robotics, 2010, 23(1):3-20.
[9] FARENZENA M, BARTOLI A, MEZOUAR Y.Efficient camera smoothing in sequential structurefrom-motion using approximate cross-validation[C]//European Conference on Computer Vision.Marseille, France:Springer-Verlag, 2008:196-209.
[10] ZHOU J M, ZHAO X M, XU Z G, et al. Algorithm of vehicle trajectory extraction using fusion of Hu moments and grayscale feature[J]. Information Technology Journal, 2014, 13(11):1770-1778.
[11] SATO J, TAKAHASHI T, IDE I, et al. Change detection in streetscapes from GPS coordinated omnidirectional image sequences[C]//Proceedings of the18th International Conference on Pattern Recognition.Hong Kong, China:IEEE, 2006:935-938.
[12] UCHIYAMA H, TAKAHASHI T, IDE I, et al.Frame registration of in-vehicle normal camera with omni-directional camera for self-position estimation[C]//International Conference on Innovative Computing Information and Control. Dalian, China:IEEE,2008:7.
[13] UCHIYAMA H, DEGUCHI D, TAKAHASHI T, et al. Ego-localization using streetscape image sequences from in-vehicle cameras[C]//Intelligent Vehicles Symposium. Xi’an, China:IEEE, 2009:185-190.
[14] ZHOU J M, ZHAO X M, WANG Z, et al. An automatic calibration system of vehicle motion error under GPS blind area[C]//International Conference on Intelligent Transportation Systems. Las Palmas, Spain:IEEE, 2015:238-243.
[15] SUN J. Research on content-based image retrieval[D].Xi’an, China:School of Computer Science, Xidian University, 2005(in Chinese).
[16] LIU Y, EMOTO H, FUJII T, et al. A method for content-based similarity retrieval of images using two dimensional DP matching algorithm[C]//International Conference on Image Analysis and Processing. Palermo, Italy:IEEE, 2001:236-241.