Monte-Carlo Localization in Underground Parking Lots using Parking Slot Numbers

Autonomous Valet Parking (AVP) in an under- ground garage is an emerging smart vehicle solution that the community believes to be solvable with close-to-market sensors. Absence of GPS signals and a high degree of self-similarity however render global visual localization in such environments a highly challenging problem. We present a novel underground parking localization method that relies on text recognition in the wild as well as optical character recognition (OCR) to automatically detect parking slot numbers. The detected numbers are then correlated with both geometric as well as semantic information extracted from an offline map of the environment. The resulting measurement model is embedded into a probabilistic Monte-Carlo localization framework. The success of our method is demonstrated on multiple real-world sequences in one of the largest underground parking garages in Shanghai.