Indoor Route And Location Inference Using Smartphone Imu Sensors

In this paper, we devise a framework to infer a smartphone user's location and walking routes in an indoor environment, using only the information from inertial measurement unit (IMU) sensors like gyroscope and accelerometer. To overcome the shortcoming of estimation drift over time in common PDR (pedestrian dead reckoning)-IMU systems, we propose a map-aided system which uses the map elements to help correct the user's position. We generate a map based on the environment parameters and a map matching algorithm is applied to find the most likely location of the user. The reading from the IMU sensors contains amounts of noise when user is walking, therefore we propose an edge detection algorithm based on the pruned exact linear time (PELT) model to smooth the piece-wise signals and identify the time frame when the user is making a turn. We evaluate our system when the smartphone is held either in the user's hand or in the backpack, and the system is able to give the correct walking path in both cases.