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We present an approach to tree recognition and localization in orchard environments for tree‐crop applications. The primary objective is to develop a pipeline for building detailed orchard maps and an algorithm to match subsequent lidar tree scans to the prior database, enabling correct data association for precision agricultural applications. Although global positioning systems (GPS) offer a simple solution, they are often unreliable in canopied environments due to satellite occlusion. The proposed method builds on the natural structure of the orchard. Lidar data are first segmented into individual trees using a hidden semi‐Markov model. Then a descriptor for representing the characteristics or appearance of each tree is introduced, allowing a hidden Markov model based matching method to associate new observations with an existing map of the orchard. The tree recognition method is evaluated on a 2.3 hectare section of an almond orchard in Victoria, Australia, over a period spanning 16 months, with a combined total of 17.5 scanned hectares and 26 kilometers of robot traversal. The results show an average matching performance of 86.8% and robustness both to segmentation errors and measurement noise. Near perfect recognition and localization (98.2%) was obtained for data sets taken one full year apart, where the seasonal variation of appearance is minimal.
Underwood et al. (Wed,) studied this question.