Localization in agricultural environments is challenging due to their unstructured nature and lack of distinctive landmarks. Although agricultural settings have been studied in the context of object classification and segmentation, the place recognition task for mobile robots is not trivial in the current state of the art. In this study, we propose MinkUNeXt-VINE, a lightweight, deep-learning-based method that surpasses state-of-the-art methods in vineyard environments thanks to its pre-processing and Matryoshka Representation Learning multi-loss approach. Our method prioritizes enhanced performance with low-cost, sparse LiDAR inputs and lower-dimensionality outputs to ensure high efficiency in real-time scenarios. Additionally, we present a comprehensive ablation study of the results on various evaluation cases and two extensive long-term vineyard datasets employing different LiDAR sensors. The results demonstrate the efficiency of the trade-off output produced by this approach, as well as its robust performance on low-cost and low-resolution input data. The code is publicly available for reproduction on a Github repository. 1 1 https://github.com/JudithV/MinkUNeXt-VINE . • 3D coordinate precision is more critical in vineyards than in urban environments. • Simpler deep learning models create effective descriptors for repetitive settings. • Matryoshka Representation Learning boosts global descriptor discriminativeness. • Multi-loss functions ensure robustness for sparse data from low-cost sensors.
Vilella-Cantos et al. (Wed,) studied this question.