4-DOF Magnetic Field Based Localization for UAV Navigation

Gietler, Harald; Böhm, Christoph; Mitterer, Tobias; Faller, Lisa-Marie; Weiß, Stephan; Zangl, Hubert

doi:10.1007/978-3-030-33950-0_20

Cited by 3 publications

(5 citation statements)

References 8 publications

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“…The distance between the first and the third emitter s 3 is given by p 13 = [−0.02, 0.03, 0.02] T . The position of s 1 is used as target position x s following the trajectory simulated according to (8). A period of 20 s using τ = 0.1 s with parameters…”

Section: Tracking Experimentsmentioning

confidence: 99%

“…In the last experiment, a trajectory, which is not randomly generated, is used. Instead of using the Brownian motion model from (8), a helical line with constant vertical velocity is used. In addition, the angular velocity is constant, but its x and y components vary periodically.…”

Section: Tracking Experimentsmentioning

confidence: 99%

“…The drawback is the limited range, but usually, the accuracy increases with the decreasing distance. Thus, they are beneficial to the robotics field [8], [9] especially when using them complementary to existing systems such as visual-inertial odometry (VIO). This article introduces a robust electromagnetic field-based localization concept.…”

Section: Introductionmentioning

confidence: 99%

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Robust Electromagnetic Pose Estimation for Robotic Applications

Gietler

Ammari

Zangl

2020

IEEE Trans. Instrum. Meas.

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A wireless electromagnetic field-based sensor system is proposed, which enables the tracking of moving objects, e.g., drones. The gathered up to 6-degrees of freedom information is complementary to existing sensing principles, e.g., global positioning system (GPS) or vision-based systems. In addition, it can be used for stand-alone navigation or noninvasive 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 the induced eddy currents. In general, unmanned aerial vehicles or wheeled robots mainly consist of conductive materials, which cause a significant estimation error. This article introduces an interference-aware electromagnetic near-fieldbased pose estimation approach. Specifically, the change in the magnetic field due to close conductive and ferromagnetic objects is modeled. 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 concepts are shown using an extended Kalman filter. It is observed that the tracking performance using the introduced model outperforms the traditional model in eddy current scenarios significantly.

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Section: Tracking Experimentsmentioning

confidence: 99%

Section: Tracking Experimentsmentioning

confidence: 99%

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Robust Electromagnetic Pose Estimation for Robotic Applications

Gietler

Ammari

Zangl

2020

IEEE Trans. Instrum. Meas.

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“…Related magnetic sensing principles were shown in [6] where 3-axis magnetic sensor arrays are used. In [7] a detailed presentation of the considered magnetic sensor is given. The employed processing and read-out hardware, which is based on a Software Defined Radio (SDR) platform, are presented in [8].…”

Section: Related Workmentioning

confidence: 99%

Artificial Landmarks for Autonomous Vehicles Based on Magnetic Sensors

et al. 2018

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We propose to use an integration process based on Transducer Electronic Data Sheets applied to a magnetic sensor system for the realization of artificial landmarks. Magnetic sensors provide an advantageous alternative in surroundings where GPS and optical sensors do not work. These landmarks can be used by passing autonomous vehicles, e.g., drones, for re-orientation and re-calibration. To facilitate the usage of these landmarks also by any vehicle, known or unknown, a standardized process for automatic connection and identification of the landmarks is suggested. During this process, all necessary information such as protocols, calibration data etc. is made known to the vehicle passing by. Based on the provided information, the vehicle itself can decide whether and how to use the provided sensory information.

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“…Optimization of artificial landmark placement is discussed in [12]. Concerning the magnetic sensing, related magnetic sensing principles were shown in [13] where 3-axis magnetic sensor arrays are used, and in [14] a detailed presentation of the considered magnetic sensor is given. A more detailed description of the employed processing and read-out hardware, which is based on a Software Defined Radio (SDR) platform, is presented in [15].…”

Section: Introductionmentioning

confidence: 99%

Artificial Landmarks for Trusted Localization of Autonomous Vehicles Based on Magnetic Sensors

Mitterer¹,

Gietler²,

Faller³

et al. 2019

Sensors

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Magnetic sensors provide an advantageous alternative localization method, primarily focusing on localization in surroundings where GPS, other radio frequency-based, as well as optical localization do not work or has severe limitations. Suitable for distances in the meter range, such magnetic localization may in particular be useful as artificial landmarks, e.g., for automatic drift correction. To easily use such artificial landmarks, we propose an integration process based on Transducer Electronic Data Sheets. With this approach, the landmarks can be used by passing autonomous vehicles, e.g., UAVs, for re-orientation and re-calibration. During this process, all necessary information such as data formats, reference coordinates, calibration data, provider of the landmark etc. is made known to the vehicle passing by. Based on the provided so-called meta-information, the vehicle itself can decide whether and how to use the provided sensory information. To provide a certain level of trust in the landmarks and their provided information, the corresponding data sheets are certified using a digital signature.

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4-DOF Magnetic Field Based Localization for UAV Navigation

Cited by 3 publications

References 8 publications

Robust Electromagnetic Pose Estimation for Robotic Applications

Robust Electromagnetic Pose Estimation for Robotic Applications

Artificial Landmarks for Autonomous Vehicles Based on Magnetic Sensors

Artificial Landmarks for Trusted Localization of Autonomous Vehicles Based on Magnetic Sensors

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