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Mission-Oriented Sensor Networks and Systems: Art and Science pp 597–631Cite as

Robot and Drone Localization in GPS-Denied Areas

Part of the Studies in Systems, Decision and Control book series (SSDC,volume 164)

Abstract

Robots and drones have recently become commonplace, with more advanced and affordable units available every year. While initially they may have been marketed primarily to hobbyists, consumer robots (and drones in particular) have become much more than just toys; they have garnered more and more attention from researchers across fields such as environmental sensing, surveillance, computer vision, machine learning, systems engineering, and networking. Robots and drones provide a rich playground in which to tackle challenging problems aimed at increasing the autonomy of machines. Moreover, as these machines become ever more ubiquitous, there arises both the desire and need to provide a way for them to coordinate their movements and actions so that they can accomplish tasks such as navigating without collision or mapping an area. Such coordination becomes more difficult once the space that must be navigated is in a GPS-denied area. In this chapter, the many facets of robot and drone coordination in GPS-denied areas are discussed, addressing issues associated with localization and coordinating multiple agents as they attempt to accomplish a common goal.

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Notes

  1. 1.

    The word drone has historically referred to unmanned aircraft (military aircraft in particular), but aside from the form of propulsion, there is an extraordinary amount of overlap between what we refer to as drones and what we normally call robots. Because of this, as well as the militaristic implications of the word drone, we will hereon refer to flying or airborne robots as simply robots unless the form of propulsion is of importance.

  2. 2.

    The chapter “Robotic Wireless Sensor Networks” dives deeply into various communication challenges and serves as an excellent companion to this chapter.

  3. 3.

    http://pdcc.ntu.edu.sg/wands/Atheros/.

  4. 4.

    https://github.com/libing64/manifold_linux.

  5. 5.

    http://www.timedomain.com/.

  6. 6.

    https://www.pozyx.io/.

  7. 7.

    http://www.decawave.com/.

  8. 8.

    http://inilabs.com/products/dynamic-vision-sensors/.

  9. 9.

    http://robotsforroboticists.com/camera-lens-selection/, http://www.baslerweb.com/en/vision-campus/camera-selection.

  10. 10.

    For more on LSD-SLAM see http://vision.in.tum.de/research/vslam/lsdslam.

  11. 11.

    For more information on the ORB-SLAM project visit http://webdiis.unizar.es/~raulmur/orbslam/.

  12. 12.

    or the more recent DDF-SAM 2.0.

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  1. University of Notre Dame, South Bend, USA

    Josh Siva & Christian Poellabauer

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  1. Josh Siva
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  1. Wireless Sensor and Mobile Ad-hoc Network Applied Cryptography Engineering (WiSeMAN-ACE) Research Lab, Department of Electrical Engineering and Computer Science, Frank H. Dotterweich College of Engineering, Texas A&M University-Kingsville, Kingsville, TX, USA

    Prof. Habib M. Ammari

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Siva, J., Poellabauer, C. (2019). Robot and Drone Localization in GPS-Denied Areas. In: Ammari, H. (eds) Mission-Oriented Sensor Networks and Systems: Art and Science. Studies in Systems, Decision and Control, vol 164. Springer, Cham. https://doi.org/10.1007/978-3-319-92384-0_17

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