BVE + EKF: A viewpoint estimator for the estimation of the object's position in the 3D task space using Extended Kalman Filters
This work addresses the challenge of robust 3D object localization for robotics in unpredictable environments, though it appears incremental as it builds on established EKF techniques.
The paper tackles the problem of estimating 3D object positions in open-field environments where RGB-D sensors struggle, proposing a Gaussian viewpoint estimator (BVE) combined with an Extended Kalman Filter (EKF) as an alternative to complex deep learning methods. The method achieved a maximum average Euclidean error of about 32 mm in experiments with artificial Gaussian noise.
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.