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

T. Barros, L. Garrote, P. Conde, M.J. Coombes, C. Liu, C. Premebida, U.J. Nunes

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.

Citations

Preprint: https://arxiv.org/abs/2303.00477