Navigate-and-Seek: a Robotics Framework for People Localization in Agricultural Environments

TL;DR

This paper presents a robotics framework that combines topological particle filtering, multi-sensor data integration, and active sensing to accurately localize and track human workers in agricultural environments, enhancing farm automation efficiency.

Contribution

It introduces an expanded topological particle filter that integrates heterogeneous sensors and active perception for improved human localization in farms.

Findings

Enhanced localization accuracy over previous methods

Effective multi-sensor data fusion in farm environments

Successful validation in real-world agricultural settings

Abstract

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.