Modeling of Magnetic Fields and Extended Objects for Localization Applications

Abstract

Cover illustration: The cover shows a magnetic dipole field (3.6), which is used as the sensor model in Paper A. The dipole is placed at the height of the front cover with position r 0 = [104 mm, 90 mm, 0 mm] relative to the bottom left corner of the front cover, with the magnetic dipole moment m = [0. 85, 0.53, 0] being orthogonal to its surface. A 3D cutout of the scalar potential for the magnetic dipole ϕ(r) = (r·m) r 3 is displayed on front, back and side cover, where r is the displacement relative to the position of the magnetic dipole. On the front cover, the field lines of the magnetic dipole field are overlaid.Linköping studies in science and technology. Dissertations.No. 1723 Modeling of Magnetic Fields and Extended Objects for Localization Applications AbstractThe level of automation in our society is ever increasing. Technologies like selfdriving cars, virtual reality, and fully autonomous robots, which all were unimaginable a few decades ago, are realizable today, and will become standard consumer products in the future. These technologies depend upon autonomous localization and situation awareness where careful processing of sensory data is required. To increase efficiency, robustness and reliability, appropriate models for these data are needed. In this thesis, such models are analyzed within three different application areas, namely (1) magnetic localization, (2) extended target tracking, and (3) autonomous learning from raw pixel information. Magnetic localization is based on one or more magnetometers measuring the induced magnetic field from magnetic objects. In this thesis we present a model for determining the position and the orientation of small magnets with an accuracy of a few millimeters. This enables three-dimensional interaction with computer programs that cannot be handled with other localization techniques.Further, an additional model is proposed for detecting wrong-way drivers on highways based on sensor data from magnetometers deployed in the vicinity of traffic lanes. Models for mapping complex magnetic environments are also analyzed. Such magnetic maps can be used for indoor localization where other systems, such as gps, do not work.In the second application area, models for tracking objects from laser range sensor data are analyzed. The target shape is modeled with a Gaussian process and is estimated jointly with target position and orientation. The resulting algorithm is capable of tracking various objects with different shapes within the same surveillance region.In the third application area, autonomous learning based on high-dimensional sensor data is considered. In this thesis, we consider one instance of this challenge, the so-called pixels to torques problem, where an agent must learn a closedloop control policy from pixel information only. To solve this problem, highdimensional time series are described using a low-dimensional dynamical model. Techniques from machine learning together with standard tools from control theory are used to autonomously design a controller f...

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