  RGB-D sensors face multiple challenges operating under open-field
environments because of their sensitivity to external perturbations such as
radiation or rain. Multiple works are approaching the challenge of perceiving
the 3D position of objects using monocular cameras. However, most of these
works focus mainly on deep learning-based solutions, which are complex,
data-driven, and difficult to predict. So, we aim to approach the problem of
predicting the 3D objects' position using a Gaussian viewpoint estimator named
best viewpoint estimator (BVE) powered by an extended Kalman filter (EKF). The
algorithm proved efficient on the tasks and reached a maximum average Euclidean
error of about 32 mm. The experiments were deployed and evaluated in MATLAB
using artificial Gaussian noise. Future work aims to implement the system in a
robotic system.
