In this paper, we propose a novel solution for the low-cost indoor localization and tracking problem using radio signal strength indicator, Inertial Measurement Unit (IMU), and magnetometer sensors available in smart handheld devices such as smartphones. The proposed algorithm does not require any sensor calibration and performs real-time. We develop a novel probabilistic IMU motion model as the proposal distribution of the sequential Monte-Carlo technique to track the device trajectory. Our algorithm can globally localize and track a device 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 device trajectory and the map of BLE beacons, we recover a continuous and smooth trajectory of the device, precise locations of the BLE beacons, and the time-varying IMU bias. The experimental results achieved using hardware show that through feeding back the optimized map and recovered IMU bias values to the Front-end, the overall system performance reaches to about 1.35 m at the third quartile of the cumulative distribution function of the localization error over the experimental data.