The agricultural domain offers a working environment where many human laborers are nowadays employed to maintain or harvest crops, with huge potential for productivity gains through the introduction of robotic automation. Detecting and localizing humans reliably and accurately in such an environment, however, is a prerequisite to many services offered by fleets of mobile robots collaborating with human workers. Consequently, in this letter, we expand on the concept of a topological particle filter (TPF) to accurately and individually localize and track workers in a farm environment, integrating information from heterogeneous sensors and combining local active sensing (exploiting a robot's onboard sensing employing a Next-Best-Sense planning approach) and global localization (using affordable IoT GNSS devices). We validate the proposed approach in topologies created for the deployment of robotics fleets to support fruit pickers in a real farm environment. By combining multi-sensor observations on the topological level complemented by active perception through the NBS approach, we show that we can improve the accuracy of picker localization in comparison to prior work.

中文翻译：

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Navigate-and-Seek：农业环境中人员定位的机器人框架

农业领域提供了一个工作环境，现在许多人工受雇于维护或收获农作物，通过引入机器人自动化具有巨大的生产力提高潜力。然而，在这样的环境中可靠、准确地检测和定位人类，是与人类工作者合作的移动机器人车队提供的许多服务的先决条件。因此，在这封信中，我们扩展了拓扑粒子滤波器 (TPF) 的概念，以在农场环境中准确、单独地定位和跟踪工人，整合来自异构传感器的信息并结合局部主动传感（利用机器人的车载传感，采用Next-Best-Sense 规划方法）和全球本地化（使用经济实惠的 IoT GNSS 设备）。我们在为部署机器人车队以支持真实农场环境中的水果采摘者而创建的拓扑中验证了所提议的方法。通过将拓扑级别的多传感器观察与通过 NBS 方法的主动感知相结合，我们表明与之前的工作相比，我们可以提高选择器定位的准确性。