Robust localization in unstructured environments, such as agricultural fields, is a critical challenge for autonomous systems. LiDAR sensors provide detailed 3D information about the environment and are invariant to lighting conditions. For this reason, LiDAR-based place recognition methods have gained significant attention. In this paper, we propose MinkUNeXt-VINE++, a novel approach that combines early fusion of heterogeneous LiDAR data from two sensors (Livox Mid-360 and Velodyne VLP-16) and a learned re-ranking strategy in inference time. This fusion leverages the strengths of each sensor to provide a more comprehensive representation of the environment. Additionally, the re-ranking approach is particularly important in repetitive environments, such as vineyards, as finding true positives is a major challenge. We evaluated our approach using the TEMPO-VINE dataset, which provides heterogeneous LiDAR data in vineyard environments across different phenological stages. Our results demonstrate that MinkUNeXt-VINE++ significantly improves place recognition performance compared to single-sensor approaches and state-of-the-art methods. MinkUNeXt-VINE++ achieves a 20% improvement in the Recall@1 metric compared to single-sensor approaches, and +30% including re-ranking. The code of our method is publicly available for reproduction.

翻译：在非结构化环境（如农业田地）中实现鲁棒定位，是自主系统面临的重大挑战。LiDAR传感器可提供环境的三维细节信息且不受光照条件影响，基于LiDAR的地点识别方法因此受到广泛关注。本文提出MinkUNeXt-VINE++方法，该方法融合两种异构LiDAR传感器（Livox Mid-360与Velodyne VLP-16）的早期数据，并在推理阶段采用学习型重排序策略。该融合机制利用各传感器优势，构建更全面的环境表征。特别地，在葡萄园等高重复性场景中，重排序策略至关重要——因正确正样本的识别是主要难点。我们采用TEMPO-VINE数据集进行评估，该数据集包含不同物候期葡萄园环境的异构LiDAR数据。结果表明，相较于单传感器方法与现有最优方法，MinkUNeXt-VINE++显著提升了地点识别性能：Recall@1指标较单传感器方法提升20%，结合重排序策略后提升30%。本方法代码已开源供复现。