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Robust and reliable place recognition and loop closure detection inagricultural environments is still an open problem. In particular, orchards area difficult case study due to structural similarity across the entire field. Inthis work, we address the place recognition problem in orchards resorting to 3DLiDAR data, which is considered a key modality for robustness. Hence, wepropose ORCHNet, a deep-learning-based approach that maps 3D-LiDAR scans toglobal descriptors. Specifically, this work proposes a new global featureaggregation approach, which fuses multiple aggregation methods into a robustglobal descriptor. ORCHNet is evaluated on real-world data collected inorchards, comprising data from the summer and autumn seasons. To assess therobustness, We compare ORCHNet with state-of-the-art aggregation approaches ondata from the same season and across seasons. Moreover, we additionallyevaluate the proposed approach as part of a localization framework, whereORCHNet is used as a loop closure detector. The empirical results indicatethat, on the place recognition task, ORCHNet outperforms the remainingapproaches, and is also more robust across seasons. As for the localization,the edge cases where the path goes through the trees are solved whenintegrating ORCHNet as a loop detector, showing the potential applicability ofthe proposed approach in this task. The code and dataset will be publiclyavailable at:\url{https://github.com/Cybonic/ORCHNet.git}

ORCHNet: A Robust Global Feature Aggregation approach for 3D LiDAR-based Place recognition in Orchards