SLAM method of indoor navigation based on Bluetooth beacon localization

One of the problems associated with the implementation of indoor location detection systems is the time-consuming procedure of equipment adjustment, which includes indoor map construction, radio signal map creation and calibrating signal propagation model. Thus, the equipment adjustment is a time-consuming and expensive process that must be performed again when there are changes in equipment configuration and allocation. The developed method provides navigation of the user inside a room and at the same time allows to build radio map and put Bluetooth beacons on the map of a room. The user's navigation inside the room is provided using a combination of PDR based on the built-in smartphone sensors, multilateration and fingerprinting. To solve the problem of determining the location of Bluetooth beacon, the Random Forest algorithm is used, which uses signal levels, user rotation angles and distance to Bluetooth beacon as a training dataset. Based on the radio map and Bluetooth beacon locations, the geometric parameters of a room are estimated.