Abstract: A probabilistic motion model to calculate the motion parameters of a user's hand held device using a noisy low cost Inertial Measurement Unit (IMU) sensor. Also described is a novel technique to reduce the bias noise present in the aforesaid IMU sensor signal, which results in a better performance of the motion model. The system utilizes a Particle Filter (PF) loop, which fuses radio signal data accumulated from Bluetooth Low Energy (BLE) beacons using BLE receiver with the signal from IMU sensor to perform localization and tracking. The Particle Filter loop operates based on a Sequential Monte Carlo technique well known to persons of ordinary skill in the art. The described approach provides a solution for both the noise problem in the IMU sensor and a motion model utilized in the Particle Filter loop, which provides better performance despite the noisy IMU sensor.

Abstract: A system and method, which utilizes incremental smoothing and mapping (iSAM) algorithm and automatically builds a beacon location map using various sensor and environmental information. The aforesaid iSAM algorithm fuses received signal strength indicator (RSSI) values available from different beacons in the environment and the information provided by the IMU sensor. The aforesaid iSAM algorithm is capable of simultaneously generating beacon and landmark map and localize the mobile computing device in the environment without having any prior information about any beacon locations. To accommodate for noisy sensor data and achieve better convergence for the iSAM algorithm, the system uses a prior beacon location map, which contains location information of some of the BLE beacons located in the environment. These known beacon locations provide cleaner environmental information to the iSAM algorithm and hence improve the overall estimation of beacon locations, which were not available apriori.

Abstract: A probabilistic motion model to calculate the motion parameters of a user's hand held device using a noisy low cost Inertial Measurement Unit (IMU) sensor. Also described is a novel technique to reduce the bias noise present in the aforesaid IMU sensor signal, which results in a better performance of the motion model. The system utilizes a Particle Filter (PF) loop, which fuses radio signal data accumulated from Bluetooth Low Energy (BLE) beacons using BLE receiver with the signal from IMU sensor to perform localization and tracking. The Particle Filter loop operates based on a Sequential Monte Carlo technique well known to persons of ordinary skill in the art. The described approach provides a solution for both the noise problem in the IMU sensor and a motion model utilized in the Particle Filter loop, which provides better performance despite the noisy IMU sensor.

Abstract: A system and method, which utilizes incremental smoothing and mapping (iSAM) algorithm and automatically builds a beacon location map using various sensor and environmental information. The aforesaid iSAM algorithm fuses received signal strength indicator (RSSI) values available from different beacons in the environment and the information provided by the IMU sensor. The aforesaid iSAM algorithm is capable of simultaneously generating beacon and landmark map and localize the mobile computing device in the environment without having any prior information about any beacon locations. To accommodate for noisy sensor data and achieve better convergence for the iSAM algorithm, the system uses a prior beacon location map, which contains location information of some of the BLE beacons located in the environment. These known beacon locations provide cleaner environmental information to the iSAM algorithm and hence improve the overall estimation of beacon locations, which were not available apriori.