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

翻译：农业环境中鲁棒且可靠的位置识别与闭环检测仍是一个悬而未决的问题。特别是果园，由于整个区域结构相似性极高，成为极具挑战性的研究场景。本文利用3D激光雷达数据（被视为实现鲁棒性的关键模态）解决果园中的位置识别问题。为此，我们提出ORCHNet——一种基于深度学习的框架，可将3D激光雷达扫描映射为全局描述符。具体而言，本文提出一种新的全局特征聚合方法，通过融合多种聚合策略生成鲁棒的全局描述符。ORCHNet在夏季和秋季采集的果园真实数据上进行了评估。为检验鲁棒性，我们将其与当前最先进的聚合方法在同季节及跨季节数据上进行对比。此外，该方案还作为定位框架的一部分被评估——ORCHNet作为闭环检测器。实验结果表明，在位置识别任务中，ORCHNet优于其他方法，且在跨季节场景下具有更强的鲁棒性。在定位方面，将ORCHNet集成作为闭环检测器后，解决了路径穿越树木区域的边缘情况问题，证实了该方法在该任务中的潜在适用性。代码与数据集将在以下网址公开：\url{https://github.com/Cybonic/ORCHNet.git}