Localization by DKF Multi Sensor Fusion in the Uncertain Environments for Mobile Robot

Authors: Omid Sojodishijani, Saeed Ebrahimijam, Vahid Rostami

Abstract:

This paper presents an optimized algorithm for robot localization which increases the correctness and accuracy of the estimating position of mobile robot to more than 150% of the past methods [1] in the uncertain and noisy environment. In this method the odometry and vision sensors are combined by an adapted well-known discrete kalman filter [2]. This technique also decreased the computation process of the algorithm by DKF simple implementation. The experimental trial of the algorithm is performed on the robocup middle size soccer robot; the system can be used in more general environments.

Keywords: Discrete Kalman filter, odometry sensor, omnidirectional vision sensor, Robot Localization.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1081357

References:

[1] Hamid Reza Moballegh, Peiman Amin, Yousof Pakzad ''An Improvement of self localization for omnidirectional mobile Robots Using a New Odometry Sensor and Omnidirectional Vision'' CCECE 2004- CCGEI 2004, Niagara Falls, May/mai 2004 0-7803-8253-6/04/$17.00 02004 IEEE.
[2] E. Fabrizi, G. Oriolo, S. Panzieri, G. Ulivi, ''Enhanced Uncertainly Modeling for Robot Localization'' Universit`a di Roma ''La Sapienza'', Via Eudossiana 18, 00184 Roma, Italy.
[3] Ra├║l A. Lastra, Paul A. Vallejos, Javier Ruiz-del-Solar, Memer IEEE ''Self-localization and ball tracking for the robocup 4-legged league ''department of Electrical Engineering, University of Chile.
[4] Greg Welch, Gary Bishop ,''An Introduction to the Kalman Filter'' University of North Carolina at Chapel Hill Department of Computer Science SIGGRAPH 2001.
[5] B. Carter, M. Good, M. Dorohoff, J. Lew, R.L. Williams II, Paolo Gallin ''Mechanical Design and Modeling of an Omni-directional Robocup Player'' Department of mechanical Engineering Ohio University, USA2002.