Vehicle localization using mono-camera and geo-referenced traffic signs

Abstract

Vision based localization is a cost effective method for indoor and outdoor application. However, it has drift problem if none global optimization is used. We proposed a geo-referenced traffic sign based localization method, which integrated the constraints of 3D traffic signs with local bundle adjustment to reduce the drift. Comparing to global bundle adjustment, Local Bundle Adjustment(LBA) has low computational cost but suffers the drift problem for large scale localization because of the random error accumulation. We reduced the drift by means of the constraints from geo-referenced traffic signs for bundle adjustment process. The original LBA model was extended for the constraints and the traffic signs were detected in images and matched with 3D landmark database automatically. From the experiments of simulated and real images, our approach can reduce the drift and have better locating results than none-constraint LBA based localization method.