Multiple sensor fusion for mobile robot localization and navigation using the Extended Kalman Filter

2015 ◽  
Author(s):  
Ehab I. Al Khatib ◽  
Mohammad A. Jaradat ◽  
Mamoun Abdel-Hafez ◽  
Milad Roigari
Keyword(s):  
Kalman Filter ◽  
Mobile Robot ◽  
Sensor Fusion ◽  
Extended Kalman Filter ◽  
Robot Localization ◽  
Mobile Robot Localization ◽  
Multiple Sensor

scholarly journals Evaluation of Localization by Extended Kalman Filter, Unscented Kalman Filter, and Particle Filter-Based Techniques

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Inam Ullah ◽  
Xin Su ◽  
Jinxiu Zhu ◽  
Xuewu Zhang ◽  
Dongmin Choi ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Mobile Robot ◽  
Particle Filter ◽  
Extended Kalman Filter ◽  
Unscented Kalman Filter ◽  
Mobile Network ◽  
High Accuracy ◽  
Robot Localization ◽  
Network Localization ◽  
Mobile Robot Localization

Mobile robot localization has attracted substantial consideration from the scientists during the last two decades. Mobile robot localization is the basics of successful navigation in a mobile network. Localization plays a key role to attain a high accuracy in mobile robot localization and robustness in vehicular localization. For this purpose, a mobile robot localization technique is evaluated to accomplish a high accuracy. This paper provides the performance evaluation of three localization techniques named Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Particle Filter (PF). In this work, three localization techniques are proposed. The performance of these three localization techniques is evaluated and analyzed while considering various aspects of localization. These aspects include localization coverage, time consumption, and velocity. The abovementioned localization techniques present a good accuracy and sound performance compared to other techniques.

scholarly journals Enhancement of mobile robot localization using extended Kalman filter

2016 ◽  
Vol 8 (11) ◽  
pp. 168781401668014 ◽  
Author(s):  
Mohammed Faisal ◽  
Mansour Alsulaiman ◽  
Ramdane Hedjar ◽  
Hassan Mathkour ◽  
Mansour Zuair ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Mobile Robot ◽  
Extended Kalman Filter ◽  
Robot Localization ◽  
Mobile Robot Localization

scholarly journals MOBILE ROBOT LOCALIZATION USING WLAN, ODOMETRY AND GYROSCOPE DATA

2010 ◽  
pp. 22-30
Author(s):  
Julian Lategahn ◽  
Frank Kuenemund ◽  
Christof Roehrig
Keyword(s):  
Kalman Filter ◽  
Mobile Robot ◽  
Sensor Fusion ◽  
Indoor Environment ◽  
Signal Strength ◽  
Office Building ◽  
Robot Localization ◽  
Mobile Robot Localization ◽  
Radio Map ◽  
Made In

In this paper a method for estimation of position and motion of a mobile robot in an indoor environment is introduced. The proposed method uses WLAN signal strength to estimate the global position of a mobile robot in an office building. Thus signal strengths of the received access points are stored in the radio map in calibration phase. In localization phase the stored values are compared with actually measured one’s. Therefore a fingerprinting algorithm, that was introduced before, is used. The improvement of the presented work is the multi sensor fusion using Kalman filter, which enhances the accuracy of fingerprinting algorithms and tracking of the robot. For this reason odometric and gyroscopic sensors of the robot are fused with the estimated position of the fingerprinting algorithm. The paper presents the experimental results of measurements made in an office building.

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