  High precision localization is a crucial requirement for the autonomous
driving system. Traditional positioning methods have some limitations in
providing stable and accurate vehicle poses, especially in an urban
environment. Herein, we propose a novel self-localizing method using a
monocular camera and a 3D compact semantic map. Pre-collected information of
the road landmarks is stored in a self-defined map with a minimal amount of
data. We recognize landmarks using a deep neural network, followed with a
geometric feature extraction process which promotes the measurement accuracy.
The vehicle location and posture are estimated by minimizing a self-defined
re-projection residual error to evaluate the map-to-image registration,
together with a robust association method. We validate the effectiveness of our
approach by applying this method to localize a vehicle in an open dataset,
achieving the RMS accuracy of 0.345 meter with reduced sensor setup and map
storage compared to the state of art approaches. We also evaluate some key
steps and discuss the contribution of the subsystems.
