Many robotic applications require the ability to locate multiple objects in the environment, but the use of instant-by-instant identification techniques may be unreliable in variable and poorly structured contexts, such as for the majority of precision agriculture settings. Inspired by the needs of the H2020 CANOPIES projects, where robotic platforms are required to perform harvesting operations in table-grape vineyards, in this paper, we propose a framework for tracking objects of interest over time using a mobile robotic platform equipped with RGB-D camera. Specifically, we design a multi-object tracking module based on an Extended Kalman Filter (EKF) which takes into account the motion of the robot to update the estimate of the localization of the objects. We validate the approach in a realistic Unity-based simulator, where a mobile robot is tasked with tracking table-grape bunches within a vineyard environment. Additionally, we conduct preliminary tests in a laboratory setup.
Arlotta, A., Lippi, M., Gasparri, A. (2023). An EKF-based Multi-Object Tracking Framework for a Mobile Robot in a Precision Agriculture Scenario. In 2023 European Conference on Mobile Robots (ECMR) (pp.319-324). 345 E 47TH ST, NEW YORK, NY 10017 USA : IEEE [10.1109/ECMR59166.2023.10256338].