Monocular localization in urban environments using road markings

Localization is an essential problem in autonomous navigation of self-driving cars. We present a monocular vision based approach for localization in urban environments using road markings. We utilize road markings as landmarks instead of traditional visual features (e.g. SIFT) to tackle the localization problem because road markings are more robust against changes in perspective, illumination, and across time. Specifically, we employ Chamfer matching to register edges of road markings against a lightweight 3D map where road markings are represented as a set of sparse points. By only matching geometry of road markings, our localization algorithm further gains robustness against photometric appearance changes in the environment. We take vehicle odometry and epipolar geometry constraints into account and formulate a non-linear optimization problem to estimate the 6 DoF camera pose. We evaluate the proposed method on data collected in the real world. Experimental results show that our method achieves sub-meter localization errors in areas with sufficient road markings.