BVE + EKF: A Viewpoint Estimator for the Estimation of the Object's Position in the 3D Task Space Using Extended Kalman Filters

• Type: Article in International Conference Proceedings Book
• Date: 2024

Authors

Magalhães, SC (Author) Other; António Paulo Moreira (Author) FEUP; Filipe Neves Santos (Author) FEUP; Dias, J (Author) Other

Conference proceedings International

Title: Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics, ICINCO 2024, Porto, Portugal, November 18-20, 2024, Volume 1.

Pages: 157-165

Indexing

Scopus - 1 Citation

Other information

Authenticus ID: P-012-PFH

DOI: 10.5220/0012945800003822

Abstract (EN): 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 three-dimensional (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 three-dimensional (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 32mm. The experiments were deployed and evaluated in MATLAB using artificial Gaussian noise. Future work aims to implement the system in a robotic system. © 2024 by SCITEPRESS-Science and Technology Publications, Lda.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8