Abstract
In this paper we present an approach to tree recognition and localisation in orchard environments for tree-crop applications. The method builds on the natural structure of the orchard by first segmenting the data into individual trees using a Hidden Semi-Markov Model. Second, a descriptor for representing the characteristics of the trees is introduced, allowing a Hidden Markov Model based matching method to associate new observations with an existing map of the orchard. The localisation method is evaluated on a dataset collected in an almond orchard, showing good performance and robustness both to segmentation errors and measurement noise.
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Jagbrant, G., Underwood, J.P., Nieto, J., Sukkarieh, S. (2015). LiDAR Based Tree and Platform Localisation in Almond Orchards. In: Mejias, L., Corke, P., Roberts, J. (eds) Field and Service Robotics. Springer Tracts in Advanced Robotics, vol 105. Springer, Cham. https://doi.org/10.1007/978-3-319-07488-7_32
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DOI: https://doi.org/10.1007/978-3-319-07488-7_32
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07487-0
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