  Traditionally, there have been few options for navigational aids for the
blind and visually impaired (BVI) in large indoor spaces. Some recent indoor
navigation systems allow users equipped with smartphones to interact with low
cost Bluetoothbased beacons deployed strategically within the indoor space of
interest to navigate their surroundings. A major challenge in deploying such
beacon-based navigation systems is the need to employ a time and
labor-expensive beacon planning process to identify potential beacon placement
locations and arrive at a topological structure representing the indoor space.
This work presents a technique called IBeaconMap for creating such topological
structures to use with beacon-based navigation that only needs the floor plans
of the indoor spaces of interest. IBeaconMap employs a combination of computer
vision and machine learning techniques to arrive at the required set of beacon
locations and a weighted connectivity graph (with directional orientations) for
subsequent navigational needs. Evaluations show IBeaconMap to be both fast and
reasonably accurate, potentially proving to be an essential tool to be utilized
before mass deployments of beacon-based indoor wayfinding systems of the
future.
