PointNetPGAP-SLC: A 3D LiDAR-based Place Recognition Approach with Segment-level Consistency Training for Mobile Robots in Horticulture

This paper addresses robotic place recognition in horticultural environments using 3D-LiDAR technology and deep learning. Three main contributions are proposed: (i) a novel model called PointNetPGAP, which combines a global average pooling aggregator and a pairwise feature interaction aggregator; (ii) a Segment-Level Consistency (SLC) model, used only during training, with the goal of augmenting the contrastive loss with a context-specific training signal to enhance descriptors; and (iii) a novel dataset named HORTO-3DLM featuring sequences from orchards and strawberry plantations. The experimental evaluation, conducted on the new HORTO-3DLM dataset, compares PointNetPGAP at the sequence- and segment-level with state-of-the-art (SOTA) models, including OverlapTransformer, PointNetVLAD, and LOGG3D. Additionally, all models were trained and evaluated using the SLC. Empirical results obtained through a cross-validation evaluation protocol demonstrate the superiority of PointNetPGAP compared to existing SOTA models. PointNetPGAP emerges as the best model in retrieving the top-1 candidate, outperforming PointNetVLAD (the second-best model). Moreover, when comparing the impact of training with the SLC model, performance increased on four out of the five evaluated models, indicating that adding a context-specific signal to the contrastive loss leads to improved descriptors.

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

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}