TY - GEN
T1 - Vision based method for the localization of intelligent vehicles in loose constraint area
AU - Liu, Xiaonan
AU - Xiong, Guangming
AU - Gong, Jianwei
AU - Han, Yu
AU - Chen, Huiyan
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/8/19
Y1 - 2016/8/19
N2 - The localization is always an important research topic in the field of intelligent vehicle. This paper proposed a novel accurate localization method for intelligent vehicle navigation in loose constraint area (LCA) that uses only a single monocular camera. First, to eliminate the impact of the perspective effect and reduce the computational dimension, Harris corner feature points of the raw image are projected to the Inverse Perspective Image. Match them with feature point from the feature local map, using Normalized Cross-Correlation algorithm (NCC), calculate the optimal localization of vehicle using Random Sample Consensus algorithm (RANSAC) assisted Extended Kalman filter and then, update the feature local map. The proposed methodology is validated in the real world using an intelligent vehicle; it also has high position accuracy and robustness in the complex illumination.
AB - The localization is always an important research topic in the field of intelligent vehicle. This paper proposed a novel accurate localization method for intelligent vehicle navigation in loose constraint area (LCA) that uses only a single monocular camera. First, to eliminate the impact of the perspective effect and reduce the computational dimension, Harris corner feature points of the raw image are projected to the Inverse Perspective Image. Match them with feature point from the feature local map, using Normalized Cross-Correlation algorithm (NCC), calculate the optimal localization of vehicle using Random Sample Consensus algorithm (RANSAC) assisted Extended Kalman filter and then, update the feature local map. The proposed methodology is validated in the real world using an intelligent vehicle; it also has high position accuracy and robustness in the complex illumination.
KW - Extended Kalman filter
KW - Intelligent vehicle
KW - Loose Constraint Area
KW - Navigation
KW - Normalized Cross-Correlation algorithm (NCC)
KW - Vision
UR - https://www.scopus.com/pages/publications/84988384799
U2 - 10.1109/ICVES.2016.7548176
DO - 10.1109/ICVES.2016.7548176
M3 - Conference contribution
AN - SCOPUS:84988384799
T3 - Proceedings - 2016 IEEE International Conference on Vehicular Electronics and Safety, ICVES 2016
SP - 89
EP - 94
BT - Proceedings - 2016 IEEE International Conference on Vehicular Electronics and Safety, ICVES 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE International Conference on Vehicular Electronics and Safety, ICVES 2016
Y2 - 10 July 2016 through 12 July 2016
ER -