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Electromagnetic Near-Field based Pose Estimation for Autonomous Systems

by H. Gietler and H. Ammari and H. Zangl

(Report number 2019-29)

Abstract
Under certain circumstances delicate maneuvers in the robotics field, such as autonomous landing of unmanned aerial vehicles or calibration of robot arms, let state-of-the-art pose estimation concepts face their limits. In GPS denied environments most current state estimation approaches are based on visual-inertial odometry, which suffer from random drift, motion blur and low overlap between consecutive images. A wire-less electromagnetic field-based sensor system is proposed which enables tracking of moving objects e.g. drones. The gathered up to 6-degrees of freedom information is complementary to existing sensing principles e.g. GPS or vision-based systems. Additionally, it can be used for standalone navigation or non-invasive localization of medical devices inside the human body. The sensor system is comprised of an 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 induced eddy currents. In general, unmanned aerial vehicles or wheeled robots mainly consist of conductive materials, which causes a significant estimation error. A low-complexity method to suppress the influence of those objects is introduced. The approach is verified using a Finite Element based solution of the full Maxwell’s equations. Due to the computational savings, the methodology can be used in real-time pose estimation schemes, which is showcased using an Extended Kalman Filter.

Keywords: Computational electromagnetics, Eddy currents, Magnetic field measurement, Pose estimation, Kalman filters.

@Techreport{GAZ19_833,
  author = {H. Gietler and H. Ammari and H. Zangl},
  title = {Electromagnetic Near-Field based Pose Estimation for Autonomous Systems},
  institution = {Seminar for Applied Mathematics, ETH Z{\"u}rich},
  number = {2019-29},
  address = {Switzerland},
  url = {https://www.sam.math.ethz.ch/sam_reports/reports_final/reports2019/2019-29.pdf },
  year = {2019}
}

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