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

TL;DR

ORCHNet is a deep learning approach that combines multiple feature aggregation methods to improve 3D LiDAR-based place recognition and loop closure detection in orchard environments, demonstrating robustness across seasons and structural similarities.

Contribution

This work introduces ORCHNet, a novel global feature aggregation method that fuses multiple approaches for enhanced robustness in orchard place recognition using 3D LiDAR data.

Findings

Outperforms state-of-the-art aggregation methods in place recognition

More robust across different seasons and orchard conditions

Effectively detects loop closures in challenging orchard scenarios

Abstract

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 will be publicly available at:\url{https://github.com/Cybonic/ORCHNet.git}