3D LiDAR-based place recognition remains largely underexplored in horticultural environments, which present unique challenges due to their semi-permeable nature to laser beams. This characteristic often results in highly similar LiDAR scans from adjacent rows, leading to descriptor ambiguity and, consequently, compromised retrieval performance. In this work, we address the challenges of 3D LiDAR place recognition in horticultural environments, particularly focusing on inter-row ambiguity by introducing three key contributions: (i) a novel model, PointNetPGAP, which combines the outputs of two statistically-inspired aggregators into a single descriptor; (ii) a Segment-Level Consistency (SLC) model, used exclusively during training to enhance descriptor robustness; and (iii) the HORTO-3DLM dataset, comprising LiDAR sequences from orchards and strawberry fields. Experimental evaluations conducted on the HORTO-3DLM and KITTI Odometry datasets demonstrate that PointNetPGAP outperforms state-of-the-art models, including OverlapTransformer and PointNetVLAD, particularly when the SLC model is applied. These results underscore the model's superiority, especially in horticultural environments, by significantly improving retrieval performance in segments with higher ambiguity.

翻译：基于3D LiDAR的地点识别在园艺环境中的研究仍相对不足，这类环境因其对激光束的半穿透性而带来独特挑战。该特性常导致相邻行间产生高度相似的LiDAR扫描，进而引发描述符模糊，最终损害检索性能。本研究针对园艺环境中3D LiDAR地点识别的挑战，特别是行间模糊性问题，提出了三项关键贡献：(i) 一种新颖的模型PointNetPGAP，它将两个受统计学启发的聚合器的输出组合成单一描述符；(ii) 一种分段一致性模型，仅在训练阶段使用以增强描述符的鲁棒性；(iii) HORTO-3DLM数据集，包含来自果园和草莓田的LiDAR序列。在HORTO-3DLM和KITTI Odometry数据集上进行的实验评估表明，PointNetPGAP的性能优于包括OverlapTransformer和PointNetVLAD在内的最先进模型，尤其是在应用SLC模型时。这些结果凸显了该模型的优越性，特别是在园艺环境中，它能显著提升在模糊性较高区段的检索性能。