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.

使用基于高斯观点估计器的扩展卡尔曼滤波器，通过预测三维物体位置来解决 RGB-D 传感器在开放场景环境下的挑战，算法在实验中表现有效，最大平均欧氏距离误差约为 32 毫米，未来的工作计划是将该系统实现在机器人系统中。