Abstract
Autonomous navigation and operation of agricultural vehicles is a challenging task due to the rather unstructured environment. An uneven terrain consisting of ground and vegetation combined with the risk of non-traversable obstacles necessitates a strong focus on safety and reliability. This paper presents an object detection and terrain classification approach for classifying individual points from 3D point clouds acquired using single multi-beam lidar scans. Using a support vector machine (SVM) classifier, individual 3D points are categorized as either ground, vegetation, or object based on features extracted from local neighborhoods. Experiments performed at a local working farm show that the proposed method has a combined classification accuracy of \(91.6\,\%\), detecting points belonging to objects such as humans, animals, cars, and buildings with \(81.1\,\%\) accuracy, while classifying vegetation with an accuracy of \(97.5\,\%\).
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Acknowledgements
This research is sponsored by the Innovation Fund Denmark as part of the project “SAFE - Safer Autonomous Farming Equipment” (project no. 16-2014-0).
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Kragh, M., Jørgensen, R.N., Pedersen, H. (2015). Object Detection and Terrain Classification in Agricultural Fields Using 3D Lidar Data. In: Nalpantidis, L., Krüger, V., Eklundh, JO., Gasteratos, A. (eds) Computer Vision Systems. ICVS 2015. Lecture Notes in Computer Science(), vol 9163. Springer, Cham. https://doi.org/10.1007/978-3-319-20904-3_18
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DOI: https://doi.org/10.1007/978-3-319-20904-3_18
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