  Autonomous valet parking is a specific application for autonomous vehicles.
In this task, vehicles need to navigate in narrow, crowded and GPS-denied
parking lots. Accurate localization ability is of great importance. Traditional
visual-based methods suffer from tracking lost due to texture-less regions,
repeated structures, and appearance changes. In this paper, we exploit robust
semantic features to build the map and localize vehicles in parking lots.
Semantic features contain guide signs, parking lines, speed bumps, etc, which
typically appear in parking lots. Compared with traditional features, these
semantic features are long-term stable and robust to the perspective and
illumination change. We adopt four surround-view cameras to increase the
perception range. Assisting by an IMU (Inertial Measurement Unit) and wheel
encoders, the proposed system generates a global visual semantic map. This map
is further used to localize vehicles at the centimeter level. We analyze the
accuracy and recall of our system and compare it against other methods in real
experiments. Furthermore, we demonstrate the practicability of the proposed
system by the autonomous parking application.
