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Kalman Filter-based Sensor Fusion for Posture Stabilization of a Mobile Robot
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 Title & Authors
Kalman Filter-based Sensor Fusion for Posture Stabilization of a Mobile Robot
Jang, Taeho; Kim, Youngshik; Kyoung, Minyoung; Yi, Hyunbean; Hwan, Yoondong;
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 Abstract
In robotics research, accurate estimation of current robot position is important to achieve motion control of a robot. In this research, we focus on a sensor fusion method to provide improved position estimation for a wheeled mobile robot, considering two different sensor measurements. In this case, we fuse camera-based vision and encode-based odometry data using Kalman filter techniques to improve the position estimation of the robot. An external camera-based vision system provides global position coordinates (x, y) for the mobile robot in an indoor environment. An internal encoder-based odometry provides linear and angular velocities of the robot. We then use the position data estimated by the Kalman filter as inputs to the motion controller, which significantly improves performance of the motion controller. Finally, we experimentally verify the performance of the proposed sensor fused position estimation and motion controller using an actual mobile robot system. In our experiments, we also compare the Kalman filter-based sensor fused estimation with two different single sensor-based estimations (vision-based and odometry-based).
 Keywords
Kalman Filter;Mobile Robot;Vision System;Posture Stabilization;
 Language
Korean
 Cited by
 References
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