The application of adaptive extended Kalman filter in mobile robot localization

2016 ◽  
Author(s):  
Pi Yuzhen ◽  
Yuan Quande ◽  
Zhang Benfa
Keyword(s):  
Kalman Filter ◽  
Mobile Robot ◽  
Extended Kalman Filter ◽  
Robot Localization ◽  
Mobile Robot Localization ◽  
Adaptive Extended Kalman Filter

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

Adaptive extended Kalman filter (AEKF)-based mobile robot localization using sonar

Robotica ◽  
2000 ◽  
Vol 18 (5) ◽  
pp. 459-473 ◽  
Author(s):  
Qing-hao Meng ◽  
Yi-cai Sun ◽  
Zuo-liang Cao
Keyword(s):  
Kalman Filter ◽  
Mobile Robot ◽  
State Equation ◽  
Robot Localization ◽  
System State ◽  
Measurement Models ◽  
Mobile Robot Localization ◽  
Localization Precision ◽  
Adaptive Extended Kalman Filter ◽  
Set Up

In this paper, an AEKF algorithm is used to localize a mobile robot equipped with eight Polaroid sonars in an indoor structured environment. The system state equation and sonar measurement models used for locating the mobile robot are set up. The localization process based on the AEKF algorithm is given. Four criteria used to judge the validity of predictive measurements of sonars are presented, which can increase the probability of the matching between predictive measurements and actual measurements. Experiments show that the localization precision based on our methods is greater than that using the conventional EKF algorithm.

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