Indoor localisation for wheeled platforms based on IMU and artificially generated magnetic field

Abstract

In recent years the research on positioning and navigation systems for indoor environments has progressed rapidly. For this purpose many technologies based on e.g. UWB, WLAN, ultrasonic or infrared were utilized. However, these systems are restricted on line-of-sight (LOS) conditions due to disturbances, fading and multipath inside of buildings. Because magnetic fields are able to penetrate walls, building materials or other objects, a DC Magnetic signal based Indoor Local Positioning System (MILPS) was developed, which provides localisation in harsh indoor environments. Multiple electrical coils - representing reference stations - and tri-axial magnetometers as mobile stations are utilized. Capturing the magnetic field intensities of at least three different coils leads to the specific slope distances and finally to the observer's position. Because the current positioning algorithm is designed for stop-and-go applications originally, this contribution focuses on the sensor fusion of MILPS and an Inertial Measurement Unit (IMU) to face kinematic applications for wheeled platforms. The short time stable IMU-integrated data, which is influenced by sensor drifts and integration errors, is then supported by MILPS, which delivers positions in a low frequent update interval. To estimate a position in two dimensional environments - in the first step - an Iterative Kaiman Filter (IKF) is applied to eliminate linearization errors caused by inaccurate predictions. Therefore the dead reckoning trajectory is updated by using MILPS' distance observations. In this context first promising experiments with combinations of IMU and MILPS have been performed proving the capability of sensor integration. While acceleration and angular rate measurements lead to a state prediction (consisting of current position and velocity) external MILPS-observations are used for IMU-data support. The IKF estimates a current state in respect to both measurement systems' statistical information.