Lane-Level Localization Using an AVM Camera for an Automated Driving Vehicle in Urban Environments

Abstract

This paper describes a lane-level localization algorithm based on a map-matching method for application to automated driving in urban environments. The lane-level localization implies localizing the vehicle with centimeter-level accuracy. In order to achieve a satisfactory level of position accuracy with a low-cost GPS, a sensor fusion approach is essential for lane-level localization. The proposed sensor fusion approach for the lane-level localization of a vehicle uses an around view monitoring (AVM) module and vehicle sensors. The proposed algorithm consists of three parts: lane detection, position correction, and localization filter. In order to detect lanes, a commercialized AVM module is used. Since this module can acquire an image around the vehicle, it is possible to obtain accurate position information of the lanes. With this information, the vehicle position can be corrected by the iterative closest point (ICP) algorithm. This algorithm estimates the rigid transformation between the lane map and lanes obtained by AVM in real-time. The vehicle position corrected by this transformation is fused with the information of vehicle sensors based on an extended Kalman filter. For higher accuracy, the covariance of the ICP is estimated using Haralick's method. The performance of the proposed localization algorithm is verified via vehicle tests on a proving ground. Test results show that the proposed method can achieve localization centimeter-level accuracy. The proposed algorithm will be useful in the implementation of automated driving control.