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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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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