Marking-level high-definition maps (HD maps) are of great significance for
autonomous vehicles, especially in large-scale, appearance-changing scenarios
where autonomous vehicles rely on markings for localization and lanes for safe
driving. In this paper, we propose a highly feasible framework for
automatically building a marking-level HD map using a simple sensor setup (one
or more monocular cameras). We optimize the position of the marking corners to
fit the result of marking segmentation and simultaneously optimize the inverse
perspective mapping (IPM) matrix of the corresponding camera to obtain an
accurate transformation from the front view image to the bird's-eye view (BEV).
In the quantitative evaluation, the built HD map almost attains
centimeter-level accuracy. The accuracy of the optimized IPM matrix is similar
to that of the manual calibration. The method can also be generalized to build
HD maps in a broader sense by increasing the types of recognizable markings