Global Localization for Intelligent Vehicles Using Ground SURF

doi:10.16183/j.cnki.jsjtu.2019.02.011

Abstract

Abstract: Global localization is essential for intelligent vehicles as navigating on the urban road. Generally, visual localization methods are based on semantic landmarks such as lanes and stop-lines which are easily interfered by other semantic landmarks, such as arrows and zebra crossing. To solve the problem, a new global localization method using ground speeded up robust features (SURF) is proposed in this paper. Firstly, SURF extracted from bird-eye view images are fused with high-precision GPS data to create a priori map. Then, SURF extracted online are matched with the map to estimate the global localization using the iterative closest point (ICP) algorithm. Finally, the global localization is fused with other sensors data by the extented Kalman filter (EKF) for better accuracy. Experiment results show that localization can reach decimeter-level accuracy, which can meet the demand for intelligent vehicles.

Key words: speeded up robust features (SURF), iterative closest point (ICP), extended Kalman filter (EKF), localization for intelligent vehicle

CLC Number:

TP 242.6

HU Bing,YANG Ming,GUO Lindong,WANG Chunxiang,WANG Bing. Global Localization for Intelligent Vehicles Using Ground SURF[J]. Journal of Shanghai Jiaotong University, 2019, 53(2): 203-208.

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