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Faster Visual-Based Localization with Mobile-PoseNet

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Computer Analysis of Images and Patterns (CAIP 2019)

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Abstract

Precise and robust localization is of fundamental importance for robots required to carry out autonomous tasks. Above all, in the case of Unmanned Aerial Vehicles (UAVs), efficiency and reliability are critical aspects in developing solutions for localization due to the limited computational capabilities, payload and power constraints. In this work, we leverage novel research in efficient deep neural architectures for the problem of 6 Degrees of Freedom (6-DoF) pose estimation from single RGB camera images. In particular, we introduce an efficient neural network to jointly regress the position and orientation of the camera with respect to the navigation environment. Experimental results show that the proposed network is capable of retaining similar results with respect to the most popular state of the art methods while being smaller and with lower latency, which are fundamental aspects for real-time robotics applications.

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Authors and Affiliations

  1. Interdisciplinary Center for Security, Reliability and Trust (SnT), University of Luxembourg, 29, Avenue J. F. Kennedy, 1855, Luxembourg, Luxembourg

    Claudio Cimarelli, Dario Cazzato, Miguel A. Olivares-Mendez & Holger Voos

Authors
  1. Claudio Cimarelli
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  2. Dario Cazzato
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  3. Miguel A. Olivares-Mendez
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  4. Holger Voos
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Corresponding author

Correspondence to Dario Cazzato .

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Editors and Affiliations

  1. Department of Computer and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano (SA), Italy

    Mario Vento

  2. Department of Computer and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano (SA), Italy

    Gennaro Percannella

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Cimarelli, C., Cazzato, D., Olivares-Mendez, M.A., Voos, H. (2019). Faster Visual-Based Localization with Mobile-PoseNet. In: Vento, M., Percannella, G. (eds) Computer Analysis of Images and Patterns. CAIP 2019. Lecture Notes in Computer Science(), vol 11679. Springer, Cham. https://doi.org/10.1007/978-3-030-29891-3_20

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  • DOI: https://doi.org/10.1007/978-3-030-29891-3_20

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  • Online ISBN: 978-3-030-29891-3

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