Tree-SLAM：用于果园单棵树高效建图的语义目标SLAM
Tree-SLAM: semantic object SLAM for efficient mapping of individual trees in orchards
AI总结：
提出Tree-SLAM，一种果园单棵树建图的语义SLAM方法，利用实例分割和级联图数据关联检测重识别树干作为路标，融合GPS与里程计，实现18 cm低误差建图。
摘要：
单棵树的准确建图是果园精准农业的重要组成部分，它使自主机器人能够执行诸如定向操作或单棵树监测等任务。然而，创建这些地图具有挑战性，因为GPS信号在密集的树冠下通常不可靠。此外，标准的同步定位与建图（SLAM）方法在果园中难以发挥作用，因为树木重复的外观会使系统混淆，从而导致建图错误。为了解决这一问题，我们引入了Tree-SLAM，这是一种专为果园单棵树建图量身定制的语义SLAM方法。利用RGB-D图像，我们的方法通过实例分割模型检测树干，估计其位置，并使用基于级联图的数据关联算法对其进行重新识别。这些被重新识别的树干作为因子图框架中的路标，该框架集成了含噪声的GPS信号、里程计和树干观测数据。该系统生成的单棵树地图地理定位误差低至18 cm，不到种植间距的20%。所提出的方法在跨越不同季节的苹果和梨果园的多样化数据集上得到了验证，证明了在GPS信号不可靠的场景下具有高建图精度和鲁棒性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
