3D tomatoes' localisation with monocular cameras using histogram filters
Sandro Costa Magalhães Filipe Neves dos Santos António Paulo Moreira Jorge Dias
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. Therefore, in
this study, we approach the use of Histogram Filters (Bayesian Discrete
Filters) to estimate the position of tomatoes in the tomato plant. 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.