  In this paper, we proposed a novel and practical solution for the real-time
indoor localization of autonomous driving in parking lots. High-level
landmarks, the parking slots, are extracted and enriched with labels to avoid
the aliasing of low-level visual features. We then proposed a robust method for
detecting incorrect data associations between parking slots and further
extended the optimization framework by dynamically eliminating suboptimal data
associations. Visual fiducial markers are introduced to improve the overall
precision. As a result, a semantic map of the parking lot can be established
fully automatically and robustly. We experimented the performance of real-time
localization based on the map using our autonomous driving platform TiEV, and
the average accuracy of 0.3m track tracing can be achieved at a speed of 10kph.
