用移动激光雷达实时估算果树冠层体积，适应不同果园结构。
Adaptive Per-Tree Canopy Volume Estimation Using Mobile LiDAR in Structured and Unstructured Orchards
- 融合激光雷达与惯性导航，动态分割果树冠层。
- 杏仁园分割成功率达80%，腰果园达93%，与无人机数据高度一致。
- 适合果园智能管理、精准农业研究者使用。
我们提出一种基于移动激光雷达的实时单棵树冠层体积估计算法，数据在机器人日常巡检中采集。与依赖静态扫描或假设均匀布局的传统方法不同，本方法通过集成激光雷达-惯性里程计、自适应分割与几何重建，可适应复杂多变的果园结构。在两个商业果园中评估：一个为规则间距的腰果园，一个为冠层密集交错的杏仁园。采用结合DBSCAN与谱聚类的混合聚类策略，实现鲁棒的单树分割，在腰果园中成功率达93%，杏仁园为80%，且与无人机获取的冠层体积估计高度一致。该工作推动了对结构多样果园环境的可扩展、非侵入式树体监测。
原文摘要 · Abstract (English)
We present a real-time system for per-tree canopy volume estimation using mobile LiDAR data collected during routine robotic navigation. Unlike prior approaches that rely on static scans or assume uniform orchard structures, our method adapts to varying field geometries via an integrated pipeline of LiDAR-inertial odometry, adaptive segmentation, and geometric reconstruction. We evaluate the system across two commercial orchards, one pistachio orchard with regular spacing and one almond orchard with dense, overlapping crowns. A hybrid clustering strategy combining DBSCAN and spectral clustering enables robust per-tree segmentation, achieving 93% success in pistachio and 80% in almond, with strong agreement to drone derived canopy volume estimates. This work advances scalable, non-intrusive tree monitoring for structurally diverse orchard environments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。