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

Autor:	Magalhães, Sandro Costa, Moreira, António Paulo, Santos, Filipe Neves dos, Dias, Jorge
Rok vydání:	2024
Předmět:	Computer Science - Robotics Computer Science - Machine Learning Electrical Engineering and Systems Science - Systems and Control
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted to ICINCO - 21st International Conference on Informatics in Control, Automation and Robotics
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.03591 Zobrazit plný text záznamu View this record from Arxiv