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Autonomous Off-Road Navigation Using Near-Feature-Based World Knowledge Incorporation on the Example of Forest Path Detection

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Intelligent Autonomous Systems 16 (IAS 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 412))

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Abstract

This paper presents a novel approach for robust off-road navigation based on deep convolutional neural networks which are combined with OpenStreetMap data to perform a forest path-based local localization approach. Corresponding near features are used to integrate navigation relevant world knowledge into a local multi-feature map. A behavior-based controller adapts the robot’s trajectory based on available features and its detection quality. The approach was tested in the Rhineland-Palatinate forest. Different forest way detection setups were evaluated and are discussed in detail. Additionally, the autonomous mobile robot GatorX855D followed a forest trail using the resulting multi-feature map.

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Authors and Affiliations

  1. Robotics Research Lab, Department of Computer Science, TU Kaiserslautern, Kaiserslautern, Germany

    Patrick Wolf, Axel Vierling, Thorsten Ropertz, Simon Velden, Carlos Guzman & Karsten Berns

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  1. Patrick Wolf
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  2. Axel Vierling
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  3. Thorsten Ropertz
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  4. Simon Velden
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  5. Carlos Guzman
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  6. Karsten Berns
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Corresponding author

Correspondence to Patrick Wolf .

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Editors and Affiliations

  1. Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore

    Marcelo H. Ang Jr

  2. Department of Precision Engineering, University of Tokyo, Tokyo, Japan

    Hajime Asama

  3. Singapore Institute of Manufacturing Technology, Singapore, Singapore

    Wei Lin

  4. Engineering Product Development, Singapore University of Technology and Design, Singapore, Singapore

    Shaohui Foong

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Wolf, P., Vierling, A., Ropertz, T., Velden, S., Guzman, C., Berns, K. (2022). Autonomous Off-Road Navigation Using Near-Feature-Based World Knowledge Incorporation on the Example of Forest Path Detection. In: Ang Jr, M.H., Asama, H., Lin, W., Foong, S. (eds) Intelligent Autonomous Systems 16. IAS 2021. Lecture Notes in Networks and Systems, vol 412. Springer, Cham. https://doi.org/10.1007/978-3-030-95892-3_13

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