用4D动态地图追踪果树生长，精准识别果实位置与大小。
Spatio-Temporal Metric-Semantic Mapping for Persistent Orchard Monitoring: Method and Dataset
- 融合激光雷达与图像数据定位3D果实位置
- 实现96.9%苹果计数准确率，尺寸误差仅1.1厘米
- 适合果园智能监测与植物表型研究者使用
在整个生长季节对果园中的单株树或单个果实进行监测，对于植物表型分析和园艺资源优化（如化学品使用与产量估计）至关重要。我们提出一种4D时空度量-语义映射系统，整合多时段测量数据以追踪果实生长。该方法结合激光雷达-图像融合模块实现3D果实定位，并采用基于位置、视觉和拓扑信息的4D果实关联算法，提升数据关联精度。在真实果园数据上评估，本方法对60棵树上的1790个苹果实现了96.9%的果实计数准确率，平均果实尺寸估计误差为1.1厘米，相比基线模型4D数据关联精度提升23.7%。我们已公开发布一个涵盖五种果实物种全生长季的多模态数据集，网址为 https://4d-metric-semantic-mapping.org/
原文摘要 · Abstract (English)
Monitoring orchards at the individual tree or fruit level throughout the growth season is crucial for plant phenotyping and horticultural resource optimization, such as chemical use and yield estimation. We present a 4D spatio-temporal metric-semantic mapping system that integrates multi-session measurements to track fruit growth over time. Our approach combines a LiDAR-RGB fusion module for 3D fruit localization with a 4D fruit association method leveraging positional, visual, and topology information for improved data association precision. Evaluated on real orchard data, our method achieves a 96.9% fruit counting accuracy for 1,790 apples across 60 trees, a mean fruit size estimation error of 1.1 cm, and a 23.7% improvement in 4D data association precision over baselines. We publicly release a multimodal dataset covering five fruit species across their growth seasons at https://4d-metric-semantic-mapping.org/
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