This paper proposes a practical method, for evaluating positioning of outdoor mobile robots using Unscented Kalman Filter (UKF). Since the UKF method does not require the linearization process unlike EKF localization, it can minimize effects of errors caused by linearization of non-linear models for position estimation. This method enables relatively high performance position estimation, using only non-inertial sensors such as low-precision GPS and a digital compass. Effectiveness of the UKF localization method was verified through actual experiments and performance of position estimation was compared with that of the existing EKF method. Experimental results revealed the proposed method has better performance than the EKF method, and it is stable regardless of initial error size, and observation period.
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This paper introduces a new outdoor localization method for practical application to guide robots. This method uses only encoder data from the robot’s wheels and non-inertial sensors, such as GPS and a digital compass, to guarantee ease of use and economy in real world usage without cumulative error. Position and orientation information from DGPS (Differential Global Positioning System) and a digital compass are combined with encoder data from the robot’s wheels to more accurately estimate robot position using an extended Kalman filter. Conventional robot guidance methods use different types of fusion that rely on DGPS. We use a very simple and consistent method that ensures localization stability by using the validation gate to evaluate DGPS reliability and digital compass data that can be easily degraded by various noise sources. Experimental results of the localization are presented that show the feasibility and effectiveness of the methods using a real robot in real world conditions.
Citations
Citations to this article as recorded by
Indoor Localization of a Mobile Robot based on Unscented Kalman Filter Using Sonar Sensors Soo Hee Seo, Jong Hwan Lim Journal of the Korean Society for Precision Engineering.2021; 38(4): 245. CrossRef
Unscented Kalman Filter Based 3D Localization of Outdoor Mobile Robots Woo Seok Lee, Min Ho Choi, Jong Hwan Lim Journal of the Korean Society for Precision Engineering.2020; 37(5): 331. CrossRef
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Extended Kalman Filter Based 3D Localization Method for Outdoor Mobile Robots Woo Seok Lee, Min Ho Choi, Jong Hwan Lim Journal of the Korean Society for Precision Engineering.2019; 36(9): 851. CrossRef
Unscented Kalman Filter based Outdoor Localization of a Mobile Robot Woo Seok Lee, Jong Hwan Lim Journal of the Korean Society for Precision Engineering.2019; 36(2): 183. CrossRef
Dynamic Accuracy Improvement of a MEMS AHRS for Small UAVs Min-Shik Roh, Beom-Soo Kang International Journal of Precision Engineering and Manufacturing.2018; 19(10): 1457. CrossRef
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