  The ability to accurately represent and localise relevant objects is
essential for robots to carry out tasks effectively. Traditional approaches,
where robots simply capture an image, process that image to take an action, and
then forget the information, have proven to struggle in the presence of
occlusions. Methods using multi-view perception, which have the potential to
address some of these problems, require a world model that guides the
collection, integration and extraction of information from multiple viewpoints.
Furthermore, constructing a generic representation that can be applied in
various environments and tasks is a difficult challenge. In this paper, a novel
approach for building generic representations in occluded agro-food
environments using multi-view perception and 3D multi-object tracking is
introduced. The method is based on a detection algorithm that generates partial
point clouds for each detected object, followed by a 3D multi-object tracking
algorithm that updates the representation over time. The accuracy of the
representation was evaluated in a real-world environment, where successful
representation and localisation of tomatoes in tomato plants were achieved,
despite high levels of occlusion, with the total count of tomatoes estimated
with a maximum error of 5.08% and the tomatoes tracked with an accuracy up to
71.47%. Novel tracking metrics were introduced, demonstrating that valuable
insight into the errors in localising and representing the fruits can be
provided by their use. This approach presents a novel solution for building
representations in occluded agro-food environments, demonstrating potential to
enable robots to perform tasks effectively in these challenging environments.
