用视觉识别单棵树，实现果园厘米级精准定位。
Tree-SLAM: semantic object SLAM for efficient mapping of individual trees in orchards
- 通过图像分割识别树干，用图结构关联不同位置的同一棵树
- 在GPS失效环境下仍能实现18厘米定位误差
- 适合果园机器人导航与个体树木管理
精确绘制果园中单个树木的位置是精准农业的重要环节，有助于自主机器人执行定向作业或个体监测。然而，密集树冠下GPS信号常不可靠，且传统同步定位与地图构建（SLAM）方法因树木外观重复易产生误匹配。为此，我们提出针对果园单棵树建模的语义SLAM方法Tree-SLAM。该方法利用RGB-D图像，通过实例分割模型检测树干，并采用基于级联图的数据关联算法估计其位置并实现重识别。这些重识别的树干作为因子图中的特征点，融合了噪声较大的GPS、里程计及树干观测数据。系统可生成单棵树地图，地理定位误差低至18厘米，小于种植间距的20%。该方法在苹果和梨园跨季节多数据集上验证，表现出高精度与强鲁棒性，尤其适用于GPS信号不可靠场景。
原文摘要 · Abstract (English)
Accurate mapping of individual trees is an important component for precision agriculture in orchards, as it allows autonomous robots to perform tasks like targeted operations or individual tree monitoring. However, creating these maps is challenging because GPS signals are often unreliable under dense tree canopies. Furthermore, standard Simultaneous Localization and Mapping (SLAM) approaches struggle in orchards because the repetitive appearance of trees can confuse the system, leading to mapping errors. To address this, we introduce Tree-SLAM, a semantic SLAM approach tailored for creating maps of individual trees in orchards. Utilizing RGB-D images, our method detects tree trunks with an instance segmentation model, estimates their location and re-identifies them using a cascade-graph-based data association algorithm. These re-identified trunks serve as landmarks in a factor graph framework that integrates noisy GPS signals, odometry, and trunk observations. The system produces maps of individual trees with a geo-localization error as low as 18 cm, which is less than 20\% of the planting distance. The proposed method was validated on diverse datasets from apple and pear orchards across different seasons, demonstrating high mapping accuracy and robustness in scenarios with unreliable GPS signals.
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