Localization based on the Hybrid Extended Kalman Filter with a highly accurate odometry model of a mobile robot

Abstract

This paper describes an improving method for solving localization problems with a highly accurate model of a mobile robot either in an uncertainly large-scale environment. Firstly, we motivate our approach by analyzing intensively the dead-reckoning model for the tricycle robot type. Secondly, we propose the localization algorithm based on a Hybrid Extended Kalman Filter using artificial beacons. In this paper, 360 0 sensor scan is used for each observation and the odometry data is updated to estimate the robot position. Then a comparison between the real and the estimated location of beacons and analyzing of the filter's performance are taken. The simulation results show that the proposed algorithm can lead the robot to robustly navigate in uncertain environments.

Adaptive Neuro-Fuzzy Extended Kaiman Filtering for robot localization

“…To this purpose, data provided from odometric, laser range finder and MAP are combined together through EKF. The localization based on EKF proposed in the literatures [1][2][3][4][5][6][7][8][9] for the estimation of robot pose. However, a significant difficulty in designing an EKF can often be traced to incomplete a priori knowledge of the process covariance matrix k Q and measurement noise covariance matrix k R [10][11][12][13].…”