SeeTree实现果园树木精确定位，99%准确率且开源可用
SeeTree -- A modular, open-source system for tree detection and orchard localization
- 基于粒子滤波的视觉定位系统，支持车行与转向全场景追踪
- 800次实验中定位成功率99%，43次转头均正确识别转向
- 开源数据集与代码，适合农业自动化研究者使用
精准定位是精准果园管理的重要需求，但目前缺乏成熟的商用解决方案。本文提出SeeTree，一种模块化、开源的嵌入式树干检测与果园定位系统，可部署于任意农机设备。在先前基于粒子滤波的行内定位基础上，新增全园定位能力，包括行间地头转弯处理；支持视觉、GNSS或轮速里程计任一方式融合到运动模型中。在商业果园实地测试中，系统在800次试验中99%成功收敛至正确位置，即使初始粒子分布存在较大不确定性。转弯测试中，860次转向行为中99%被正确追踪（涵盖43次不同行段切换）。为促进应用与后续研发，我们公开数据集、设计文件及源码。
原文摘要 · Abstract (English)
Accurate localization is an important functional requirement for precision orchard management. However, there are few off-the-shelf commercial solutions available to growers. In this paper, we present SeeTree, a modular, open source embedded system for tree trunk detection and orchard localization that is deployable on any vehicle. Building on our prior work on vision-based in-row localization using particle filters, SeeTree includes several new capabilities. First, it provides capacity for full orchard localization including out-of-row headland turning. Second, it includes the flexibility to integrate either visual, GNSS, or wheel odometry in the motion model. During field experiments in a commercial orchard, the system converged to the correct location 99% of the time over 800 trials, even when starting with large uncertainty in the initial particle locations. When turning out of row, the system correctly tracked 99% of the turns (860 trials representing 43 unique row changes). To help support adoption and future research and development, we make our dataset, design files, and source code freely available to the community.
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