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RGB-D Odometry for Autonomous Lawn Mowing

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Artificial Intelligence and Soft Computing (ICAISC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12855))

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Abstract

Localization for outdoor mobile robots is crucial to accomplish complex tasks in difficult environments. One of the examples is an autonomous mower operating in various lawns placed in parks, airports, home gardens and many more. To ensure all navigation algorithms’ requirements are met, first accurate estimation of current position and orientation needs to be found. Scientists proposed many approaches using encoders, RADARs, LIDARs or vision/depth cameras. However, this is the first attempt to investigate odometry performance for autonomous lawn mowing using RGB-D cameras. The contribution is twofold. First, several odometry algorithms in autonomous mower environments were examined in terms of localization accuracy and execution time. Secondly, a new dataset was collected containing sequences from a city park and home lawn. The dataset contains aligned color and depth images. This study aimed to extend knowledge about RGB-D odometry and analyze how RGB-D cameras may be used in agricultural robots, where the environment is often an open space without many feature points or distinctive objects used in the odometry algorithms.

This work was supported by The National Centre for Research and Development [grant number POIR.01.01.01-00-1069/18].

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Authors and Affiliations

  1. Unitem, Wroclaw, Poland

    Marcin Ochman, Magda Skoczeń, Damian Krata, Marcin Panek, Krystian Spyra & Andrzej Pawłowski

  2. Faculty of Electronics, Wroclaw University of Science and Technology, Wroclaw, Poland

    Marcin Ochman & Magda Skoczeń

Authors
  1. Marcin Ochman
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  2. Magda Skoczeń
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  3. Damian Krata
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  4. Marcin Panek
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  5. Krystian Spyra
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  6. Andrzej Pawłowski
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Corresponding author

Correspondence to Marcin Ochman .

Editor information

Editors and Affiliations

  1. Czestochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Krakow, Poland

    Ryszard Tadeusiewicz

  6. Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Ochman, M., Skoczeń, M., Krata, D., Panek, M., Spyra, K., Pawłowski, A. (2021). RGB-D Odometry for Autonomous Lawn Mowing. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2021. Lecture Notes in Computer Science(), vol 12855. Springer, Cham. https://doi.org/10.1007/978-3-030-87897-9_8

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  • DOI: https://doi.org/10.1007/978-3-030-87897-9_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87896-2

  • Online ISBN: 978-3-030-87897-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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