MonoVisual3DFilter: 3D tomatoes' localisation with monocular cameras using histogram filters

TL;DR

This paper introduces MonoVisual3DFilter, a histogram filter-based method for localizing tomatoes using monocular cameras, demonstrating promising accuracy in simulation and laboratory tests for agricultural robotics.

Contribution

It presents a novel approach using histogram filters for 3D object localization with monocular sensors in agricultural settings, addressing limitations of RGB-D cameras.

Findings

Mean absolute error below 10 mm in simulation

Error below 20 mm in laboratory conditions

Viable for real-world agricultural applications

Abstract

Performing tasks in agriculture, such as fruit monitoring or harvesting, requires perceiving the objects' spatial position. RGB-D cameras are limited under open-field environments due to lightning interferences. So, in this study, we state to answer the research question: "How can we use and control monocular sensors to perceive objects' position in the 3D task space?" Towards this aim, we approached histogram filters (Bayesian discrete filters) to estimate the position of tomatoes in the tomato plant through the algorithm MonoVisual3DFilter. Two kernel filters were studied: the square kernel and the Gaussian kernel. The implemented algorithm was essayed in simulation, with and without Gaussian noise and random noise, and in a testbed at laboratory conditions. The algorithm reported a mean absolute error lower than 10 mm in simulation and 20 mm in the testbed at laboratory conditions with an assessing distance of about 0.5 m. So, the results are viable for real environments and should be improved at closer distances.