Sensor fusing using a convex combination of two Kalman filters - Experimental results

Autor:	Pietro Muraca, Antonio Grano, Luigi D'Alfonso, Paolo Pugliese
Rok vydání:	2013
Předmět:	Engineering business.industry Monte Carlo localization Kalman filter Simultaneous localization and mapping Sensor fusion Invariant extended Kalman filter Computer Science::Robotics Extended Kalman filter Fast Kalman filter Computer vision Artificial intelligence business Alpha beta filter
Zdroj:	ICAR
DOI:	10.1109/icar.2013.6766450
Popis:	In this work the mobile robot localization problem in an unknown environment is faced and a new version of the Extended Kalman filter is proposed to estimate the robot position and orientation. This new filter uses a convex combination of two filters estimating the same state variables. The first filter is based on measurements provided by robot on board distance sensors while the second one uses out of board distance sensors measurements. The resulting “Mixed” Kalman filter is designed to emphasize the qualities and overcome the defects of each used sensor. The proposed fusing technique has been tested in a real experimental framework using the robot Khepera III. The algorithm has been contrasted with other Extended Kalman filters, based on the on board and on the out of board sensors measurements, yielding to encouraging results.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::31fd52d9083f121d118183f3958a42f3 https://doi.org/10.1109/icar.2013.6766450 Zobrazit plný text záznamu