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UAV Localization Using Inertial Sensors and Satellite Positioning Systems

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Handbook of Unmanned Aerial Vehicles

Abstract

This chapter provides an overview of UAV localization with a focus on aided inertial localization, that is, algorithms for fusing data from, for example, satellite positioning systems, barometric sensors, and magnetometers with inertial sensors to provide real-time position and orientation. The presentation is based in the use of the Extended Kalman Filter (EKF) as the core tool for data fusion, which is commonly used for its simplicity, computational efficiency, and optimality (with respect to sensor noise characteristics). This chapter presents an example implementation of aided inertial localization on a UAV as a tutorial in the key concepts in airborne localization and as a basic guide toward more complicated implementation.

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Author information

Authors and Affiliations

  1. Australian Centre for Field Robotics, The University of Sydney, Sydney, NSW, Australia

    Mitch Bryson

  2. Australian Centre for Field Robotics (ACFR), School of Aerospace, Mechanical & Mechatronic Engineering (AMME), The University of Sydney, Sydney, NSW, Australia

    Salah Sukkarieh

Authors
  1. Mitch Bryson
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  2. Salah Sukkarieh
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Corresponding authors

Correspondence to Mitch Bryson or Salah Sukkarieh .

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Denver, Denver, Colorado, USA

    Kimon P. Valavanis

  2. School of Electrical and Computer Engineering, The Georgia Institute of Technology, Atlanta, Georgia, USA

    George J. Vachtsevanos

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© 2015 Springer Science+Business Media Dordrecht

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Bryson, M., Sukkarieh, S. (2015). UAV Localization Using Inertial Sensors and Satellite Positioning Systems. In: Valavanis, K., Vachtsevanos, G. (eds) Handbook of Unmanned Aerial Vehicles. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-9707-1_3

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