| Description: |
Along with the growing number of autonomous mobile platforms, the demand of accurate localization approaches for navigational tasks increases. Recent research focuses on indoor navigation, a global positioning system (GPS)-denied environment, mostly using visual and inertial sensors for pose estimation. However, their accuracy decreases when approaching objects. Reasons are, e.g., motion blur and low overlap between consecutive images. As an alternative for close range localization, a wireless electromagnetic field-based sensor system capable of tracking moving objects, e.g. robots is presented. The gathered up to 6-degrees of freedom information is useful for stand-alone navigation, non-invasive localization of medical devices inside the human body and can be used complementary to existing sensing principles. The sensor system is comprised of an electromagnetic field exciter and a sensor. The exciter can be mounted on a moving robot and generates an electromagnetic field. The field is measured by the sensor, and subsequently, the pose of the exciter with respect to the sensors pose is estimated. Conductive objects in the vicinity of the sensor alter the measured magnetic field due to the induced eddy currents. In general, unmanned aerial vehicles or wheeled robots mainly consist of conductive materials, which can cause a significant estimation error. In this work an interference-aware electromagnetic near-field based pose estimation approach is presented. Specifically, the change in magnetic field due to close conductive and ferromagnetic objects is analytically modelled. Iterative numerical solutions of Maxwell’s equations, based on, e.g. finite-element method, are avoided. Instead, an analytic expression of the change in the magnetic field due to present eddy currents is given. The advantages of the proposed concept for model-based low-complexity pose estimation are shown by the employment of an extended Kalman filter. It is observed that the tracking performance using the introduced observation model outperforms the traditionally used magnetic dipole model in eddy current scenarios, significantly. Moreover, the main advantage of the proposed modelling approach is the reduced complexity with respect to computationally expensive FEM models which are hardly applicable in real-time applications such as motion tracking. |