Ocrapose: An Indoor Positioning System Using Smartphone/Tablet Cameras and OCR-Aided Stereo Feature Matching

In this paper, we propose an image-based localization system, applicable for a number of indoor scenarios including office buildings, airports, chain stores, etc. In such applications, text/numbers are suitable distinctive landmarks for localization. The proposed system takes advantage of OCR to read the text/numbers and provide a rough estimate using the floor plan. Next, it performs OCR-aided stereo feature matching to refine the estimate by solving a PnP problem. Experiments show that this system achieves a median localization error of less than 50 cm for test positions located as far as 7 meters from a 20cm by 30cm number plate using different test devices in a university building scenario.