AbstractsComputer Science

Interest in indoor localization is growing because it is an important component of many applications (e.g. augmented reality, customer navigation). Image-based localization, using naturally-occurring features in the environment, is an attractive solution to this problem. A challenge is to be able to perform this on a mobile device with limited computing power. Another challenge is that buildings can have interior locations with similar appearances, which can confuse an image-based recognition system. Since many applications do not need the exact location of an image, this research focuses on qualitative localization, which is the problem of determining the approximate location by matching a query image to a database of images. This paper proposes a novel approach that uses an efficient hashing scheme to quickly identify candidate locations, then applies a strong geometric constraint to reject matches that have similar appearance. Through experiments using a large campus building, the approach is shown to be able to localize a query image with high accuracy and have the potential to run in real time on a mobile device.