NDLI: Sensor data fusion using Unscented Kalman Filter for accurate localization of mobile robots

Content Provider	IEEE Xplore Digital Library
Author	Anjum, M.L. Jaehong Park Wonsang Hwang Hyun-il Kwon Jong-hyeon Kim Changhun Lee Kwang-soo Kim Dong-il Danr Cho
Copyright Year	2010
Description	Author affiliation: Department of Control and Instrumentation Engineering, Hanbat National University, Daejeon, Korea (Kwang-soo Kim) \|\| Department of Electrical Engineering and Computer Science/ASRI/ISRC, Seoul National University, Seoul, Korea (Anjum, M.L.; Jaehong Park; Wonsang Hwang; Hyun-il Kwon; Jong-hyeon Kim; Changhun Lee; Dong-il Danr Cho)
Abstract	This paper presents a sensor-data-fusion method using an Unscented Kalman Filter (UKF), to implement an accurate localization system for mobile robots. Integration of data from various sensors using an efficient sensor fusion algorithm is required to achieve a continuous and accurate localization of mobile robots. We use data from low cost accelerometer, gyroscope, and encoders to obtain robot motion information. The UKF, used as an efficient sensor fusion algorithm, is an advanced filtering technique which outperforms the widely-used Extended Kalman Filter (EKF) in many applications. The system is able to compensate for the slip errors by switching between two different UKF models built for slip and no-slip cases. Since the accelerometer error accumulates with time because of the double integration, the data from accelerometer is only used in slip model of the UKF. The results obtained from UKF sensor fusion algorithm are compared with the results from an accurate distance laser sensor. The experimental results show that the system is able to accurately track the motion of the robot in various robot motion scenarios including the scenario when robot's encoders data is not reliable due to the slip occurrence.
Starting Page	947
Ending Page	952
File Size	805329
Page Count	6
File Format	PDF
ISBN	9781424474530
e-ISBN	9788993215021
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-27
Publisher Place	Korea (South)
Access Restriction	Subscribed
Rights Holder	IEEE International Conference on Control & Automation
Subject Keyword	Accelerometers Noise Unscented Kalman Filter Robot sensing systems Mobile Robot Navigation Slip Compensation Gyroscopes Mobile robots Kalman filters Sensor Fusion
Content Type	Text
Resource Type	Article

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