  In this paper, we develop a system for the low-cost indoor localization and
tracking problem using radio signal strength indicator, Inertial Measurement
Unit (IMU), and magnetometer sensors. We develop a novel and simplified
probabilistic IMU motion model as the proposal distribution of the sequential
Monte-Carlo technique to track the robot trajectory. Our algorithm can globally
localize and track a robot with a priori unknown location, given an informative
prior map of the Bluetooth Low Energy (BLE) beacons. Also, we formulate the
problem as an optimization problem that serves as the Back-end of the algorithm
mentioned above (Front-end). Thus, by simultaneously solving for the robot
trajectory and the map of BLE beacons, we recover a continuous and smooth
trajectory of the robot, corrected locations of the BLE beacons, and the
time-varying IMU bias. The evaluations achieved using hardware show that
through the proposed closed-loop system the localization performance can be
improved; furthermore, the system becomes robust to the error in the map of
beacons by feeding back the optimized map to the Front-end.
