Localization in agricultural environments is challenging due to their unstructured nature and lack of distinctive landmarks. Although agricultural settings have been studied in the context of object classification and segmentation, the place recognition task for mobile robots is not trivial in the current state of the art. In this study, we propose MinkUNeXt-VINE, a lightweight, deep-learning-based method that surpasses state-of-the-art methods in vineyard environments thanks to its pre-processing and Matryoshka Representation Learning multi-loss approach. Our method prioritizes enhanced performance with low-cost, sparse LiDAR inputs and lower-dimensionality outputs to ensure high efficiency in real-time scenarios. Additionally, we present a comprehensive ablation study of the results on various evaluation cases and two extensive long-term vineyard datasets employing different LiDAR sensors. The results demonstrate the efficiency of the trade-off output produced by this approach, as well as its robust performance on low-cost and low-resolution input data. The code is publicly available for reproduction.

翻译：农业环境因其非结构化特性及缺乏显著地标，使得定位任务极具挑战性。尽管农业场景中的物体分类与分割任务已有研究，但在当前技术水平下，移动机器人的地点识别任务仍非易事。本研究提出MinkUNeXt-VINE——一种基于轻量级深度学习的方法，凭借其预处理流程与套娃表示学习多损失函数策略，在葡萄园环境中超越了现有最优方法。我们的方法优先考虑使用低成本、稀疏的LiDAR输入与低维度输出来提升性能，以确保实时场景下的高效率。此外，我们通过在不同评估案例及两个采用不同LiDAR传感器的长期葡萄园数据集上进行全面的消融实验，系统分析了结果。实验表明，该方法在输出效率权衡方面表现优异，并在低成本、低分辨率输入数据上展现出鲁棒性能。相关代码已公开以供复现。