Accurate robot localization relative to orchard row centerlines is essential for autonomous guidance where satellite signals are often obstructed by foliage. Existing sensor-based approaches rely on various features extracted from images and point clouds. However, any selected features are not available consistently, because the visual and geometrical characteristics of orchard rows change drastically when tree types, growth stages, canopy management practices, seasons, and weather conditions change. In this work, we introduce a novel localization method that doesn't rely on features; instead, it relies on the concept of a row-sensing template, which is the expected observation of a 3D sensor traveling in an orchard row, when the sensor is anywhere on the centerline and perfectly aligned with it. First, the template is built using a few measurements, provided that the sensor's true pose with respect to the centerline is available. Then, during navigation, the best pose estimate (and its confidence) is estimated by maximizing the match between the template and the sensed point cloud using particle-filtering. The method can adapt to various orchards and conditions by re-building the template. Experiments were performed in a vineyard, and in an orchard in different seasons. Results showed that the lateral mean absolute error (MAE) was less than 3.6% of the row width, and the heading MAE was less than 1.72 degrees. Localization was robust, as errors didn't increase when less than 75% of measurement points were missing. The results indicate that template-based localization can provide a generic approach for accurate and robust localization in real-world orchards.
翻译:相对于果园行的中线, 精确的机器人本地化对于自主指导来说至关重要, 因为星象信号经常被叶子阻塞。 现有的基于传感器的方法依赖于从图像和点云中提取的各种特征。 但是, 任何选定的特征都无法始终存在, 因为果园行的视觉和几何特征在树类型、 生长阶段、 树冠管理方法、 季节和天气条件发生变化时会发生巨大变化。 在此工作中, 我们引入一种新的本地化方法, 不依赖于特性; 相反, 它依赖于行式测量模板的概念, 即3D传感器在果园行中运行的预期观测, 当传感器位于中心线的任何地方并且与它完全吻合时。 首先, 模板是使用一些测量方法, 只要树形与中心线有关的真实面的图像和几几何特征都变化。 在导航过程中, 最佳的构成估计( 及其信心) 是通过利用粒子过滤法使模板和感测点云之间的匹配最大化。 这种方法可以适应在果园区间行中进行的各种果实和条件, 以绝对的测量结果显示在方向上, 或正值显示的结果在方向中, 。 在正态中, 度上, 实验显示结果在方向中, 度上, 度上, 显示的结果是更差差差的 。,, 显示为: 。