BVE + EKF: A viewpoint estimator for the estimation of the object's position in the 3D task space using Extended Kalman Filters

TL;DR

This paper introduces a Gaussian viewpoint estimator using Extended Kalman Filters to predict 3D object positions from monocular camera data, achieving high accuracy in simulated environments.

Contribution

The paper presents a novel BVE method combined with EKF for 3D position estimation, offering an alternative to deep learning approaches with promising accuracy.

Findings

Maximum average Euclidean error of 32 mm

Efficient performance in MATLAB simulations

Potential for robotic system implementation

Abstract

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.