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Vision-Based Self-contained Target Following Robot Using Bayesian Data Fusion

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Advances in Visual Computing (ISVC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10072))

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Abstract

Several visual following robots have been proposed in recent years. However, many require the use of several, expensive sensors and often the majority of the image processing and other calculations are performed off-board. This paper proposes a simple and cost effective, yet robust visual following robot capable of tracking a general object with limited restrictions on target characteristics. To detect the objects, tracking-learning-detection (TLD) is used within a Bayesian framework to filter and fuse the measurements. A time-of-flight (ToF) depth camera is used to refine the distance estimates at short ranges. The algorithms are executed in real-time (approximately 30 fps) in a Jetson TK1 embedded computer. Experiments were conducted with different target objects to validate the system in scenarios including occlusions and various illumination conditions as well as to show how the data fusion between TLD and the ToF camera improves the distance estimation.

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Notes

  1. 1.

    http://www.nvidia.com/object/tegra-k1-processor.html.

  2. 2.

    Although other ToF cameras such as the classic SR4000 from MESA imaging, the PMD CamCube 3.0 or SoftKinetic’s DS536A have ranges of up to 5 m, the low-cost and lightweight Senz3D was deemed sufficient for our purposes.

  3. 3.

    Note that the set points \(s_{p_u}\) and \(s_{p_z}\) correspond to the desired target position with respect to the robot, not to the actual robot position. The controllers use the set points to move the robot so that the difference between the estimated position and the set point is minimized.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Marquette University, 1551 W. Wisconsin Ave., Milwaukee, Wisconsin, 53233, USA

    Andrés Echeverri Guevara, Anthony Hoak, Juan Tapiero Bernal & Henry Medeiros

Authors
  1. Andrés Echeverri Guevara
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  2. Anthony Hoak
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  3. Juan Tapiero Bernal
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  4. Henry Medeiros
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Corresponding author

Correspondence to Henry Medeiros .

Editor information

Editors and Affiliations

  1. University of Nevada , Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center , Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory , Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute , Reno, Nevada, USA

    Darko Koracin

  5. The Australian National University , O'Malley, Aust Capital Terr, Australia

    Fatih Porikli

  6. Pilot AI Labs , Redwood City, California, USA

    Sandra Skaff

  7. University of Florida , Gainesville, Florida, USA

    Alireza Entezari

  8. Google Inc. , Mountain View, California, USA

    Jianyuan Min

  9. Osaka University , Osaka, Japan

    Daisuke Iwai

  10. The MOVES Institute , Monterey, California, USA

    Amela Sadagic

  11. University of Arizona , Tucson, Arizona, USA

    Carlos Scheidegger

  12. Université Paris-Sud , Orsay, France

    Tobias Isenberg

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Guevara, A.E., Hoak, A., Bernal, J.T., Medeiros, H. (2016). Vision-Based Self-contained Target Following Robot Using Bayesian Data Fusion. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10072. Springer, Cham. https://doi.org/10.1007/978-3-319-50835-1_76

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  • DOI: https://doi.org/10.1007/978-3-319-50835-1_76

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50834-4

  • Online ISBN: 978-3-319-50835-1

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