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    PointNetPGAP-SLC: a 3D LiDAR-based place recognition approach with segment-level consistency training for mobile robots in horticulture by T Barros, L Garrote, P Conde, Matthew Coombes, Cunjia Liu, C Premebida, UJ Nunes

    Published 2024
    “…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. …”
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