Pretty cool. You can achieve similar localization accuracy with WiFi, BLE, ZigBee, or long-range RFID. Common techniques are time of flight (ToF, or time of arrival), FMCW ranging, fingerprinting, or signal strength modeling.
Depending on the method used, the limitations arise from (a) needing to know ground truth location of beacons; (b) any necessary training steps; (c) multipath issues; (d) synchronization; or (e) other non-ideal RF artifacts.
You can also combine multiple sensor measurements over time (eg. a person's path) using bayesian inference techniques (ie. particle filters) to further improve location accuracy.
(This topic is related to my PhD thesis: mobile robots finding and interacting with long-range RFID tags in homes. Many of the techniques are akin to radar: http://www.travisdeyle.com/research.html)
Jakub again here. This is great; thanks for your comments. We try to have a very pragmatic approach and believe that room beaconifying procedure should be extremely simple, so mobile developers can concentrate on domain specific challenges regarding their use-cases.
Thus, ref (a) it is as simple deploying few beacons in the room (see this video: https://www.youtube.com/watch?v=wtBERi7Lf3c)
The floor plan and location of beacons is calculated automatically and once you save the room in the cloud you or other people can access calibration and build location-aware apps. There is also no specific training requirement.
The advantage of using BLE beacons over WiFi or ZigBee is the fact they are natively integrated with mobile operating system s (e.g. via iBeacon protocol for Apple), so a smartphone can perform calculations in the background without any user intervention - frictionless applications are possible.
Depending on the method used, the limitations arise from (a) needing to know ground truth location of beacons; (b) any necessary training steps; (c) multipath issues; (d) synchronization; or (e) other non-ideal RF artifacts.
You can also combine multiple sensor measurements over time (eg. a person's path) using bayesian inference techniques (ie. particle filters) to further improve location accuracy.
(This topic is related to my PhD thesis: mobile robots finding and interacting with long-range RFID tags in homes. Many of the techniques are akin to radar: http://www.travisdeyle.com/research.html)