  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.
