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Inertial Sensor-Based Simultaneous Localization and Mapping for UAVs

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Handbook of Unmanned Aerial Vehicles

Abstract

This chapter provides an overview of algorithms for inertial sensor-based Simultaneous Localization and Mapping (SLAM) within the context of Unmanned Aerial Vehicles (UAVs). The presentation in this chapter is based on the use of the Extended Kalman Filter (EKF) and the Extended Information Filter (EIF) due to their ease of understanding, applicability to online implementation, and prevalence in airborne localization applications outside of SLAM (such as aided inertial localization). The discussion here includes an examination of SLAM for both small- and large-scale operation over the surface of the Earth, inertial SLAM using both range-bearing and bearing-only observations of the terrain, and a look at several different centralized and decentralized architectures for performing multi-vehicle SLAM.

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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). Inertial Sensor-Based Simultaneous Localization and Mapping for UAVs. In: Valavanis, K., Vachtsevanos, G. (eds) Handbook of Unmanned Aerial Vehicles. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-9707-1_5

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