Extended Kalman Filter Sensor Fusion in Practice for Mobile Robot Localization

Self-driving vehicles and autonomously guided robots could be very beneficial to today's civilization. However, the mobile robot's position must be accurately known, which referred as the localization with the task of tracking the dynamic position, in order for the robot to be active and u...

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Bibliographic Details
Published in:International journal of advanced computer science & applications 2022, Vol.13 (2)
Main Authors: Housein, Alaa Aldeen, Xingyu, Gao, Li, Weiming, Huang, Yang
Format: Article
Language:English
Subjects:
Autonomous cars
Differential equations
Environment models
Extended Kalman filter
Lidar
Localization
Localization method
Occupancy
Range finders
Robots
State estimation
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Summary:Self-driving vehicles and autonomously guided robots could be very beneficial to today's civilization. However, the mobile robot's position must be accurately known, which referred as the localization with the task of tracking the dynamic position, in order for the robot to be active and useful. This paper presents a robot localization method with a known starting location by a real-time reconstructed environment model that represented as an occupancy grid map. The extended Kalman filter (EKF) is formulated as a nonlinear model-based estimator for fuse Odometry and a LIDAR range finder sensor. Because the occupancy grid map for the area is provided, just the inaccuracies of the LIDAR range finder will be considered. The experimental results on the “turtlebot” robot using robot operating system (ROS) show a significant improvement in the pose of the robot using the Kalman filter compared with sample Odometry. This paper also establishes the framework for using a Kalman filter for state estimation, providing all relevant mathematical equations for differential drive robot, this technique can be used to a variety of mobile robots.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2022.0130204