Fruit distribution is pivotal in shaping the future of both agriculture and agricultural robotics, paving the way for a streamlined supply chain. This study introduces an innovative methodology that harnesses the synergy of RGB imagery, LiDAR, and IMU data, to achieve intricate tree reconstructions and the pinpoint localization of fruits. Such integration not only offers insights into the fruit distribution, which enhances the precision of guidance for agricultural robotics and automation systems, but also sets the stage for simulating synthetic fruit patterns across varied tree architectures. To validate this approach, experiments have been carried out in both a controlled environment and an actual peach orchard. The results underscore the robustness and efficacy of this fusion-driven methodology, highlighting its potential as a transformative tool for future agricultural robotics and precision farming.

翻译：果实分布对于塑造农业及农业机器人的未来至关重要，为简化的供应链铺平了道路。本研究提出了一种创新方法，利用RGB图像、LiDAR和IMU数据的协同作用，实现复杂的树木重建以及果实的精确定位。这种集成不仅能够洞察果实分布，从而提升农业机器人和自动化系统的导航精度，还为在不同树形结构上模拟合成果实模式奠定了基础。为了验证该方法，我们在受控环境及真实桃园中进行了实验。结果强调了这种融合驱动方法的稳健性和有效性，凸显了其作为未来农业机器人与精准农业变革性工具的潜力。