農作業者を追いかけて特定するロボティクスフレームワーク
Navigate-and-Seek: a Robotics Framework for People Localization in Agricultural Environments
農場で働く人々を、ロボット搭載センサと安価なGNSSを組み合わせたトポロジカル粒子フィルタで個別に位置推定・追跡する手法を提案し、実農場で精度向上を実証した。
著者: Riccardo Polvara, Francesco Del Duchetto, Gerhard Neumann, Marc Hanheide
分類: cs.RO
原文アブストラクト
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 paper, 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.
