Camera-based indoor positioning using scalable streaming of compressed binary image signatures

Recent progress in the field of content-based image retrieval has enabled camera-based indoor positioning. The matching of smart-phone recordings with a database of geo-referenced images allows for meter accurate infrastructure-free localization. In mobile scenarios, however, three major constraints have to be considered: limited computational resources of mobile devices, limited network capacity and the need for scalability in large buildings. To address these issues, we modify the state-of-the-art Vector of Locally Aggregated Descriptors (VLAD) image signature to work with recently emerging binary feature descriptors. We show that this results in a substantial reduction in the overall computational complexity, which enables the matching of image signatures directly on the mobile device. The specific properties of this signature form the basis of our proposed scalable streaming approach that preemptively loads image signatures of reference images in the vicinity of the user onto the mobile device to mitigate the effect of network latency. In order to provide efficient streaming, we compress the signatures by exploiting the similarities of spatially neighboring reference images. In combination, the contributions of this paper lead to an indoor localization system, which allows instantaneous camera-based indoor positioning with very low requirements on the available network connection.