NDLI: Implementation of an adaptive EKF to multiple low cost navigation sensors in wheeled mobile robots

Content Provider	IEEE Xplore Digital Library
Author	Ashokaraj, I.A.R. Silson, P.M.G. Tsourdos, A. White, B.A.
Copyright Year	2002
Description	Author affiliation: Royal Military Coll. of Sci., Cranfield Univ., Swindon, UK (Ashokaraj, I.A.R.; Silson, P.M.G.; Tsourdos, A.; White, B.A.)
Abstract	The present aim of this research is to design a navigation sensor suite for a newly built mobile robot using low cost multiple sensors. A basic requirement for an autonomous mobile robot is its ability to localize itself accurately. This paper describes an accurate method for generating navigational data for a wheeled mobile robot. An adaptive extended Kalman filter (AEKF) is used to fuse data from multiple low cost sensors. In order to estimate the spatial position of a wheeled robot, a combination of accelerometers, a rate gyroscope and two wheel encoders are used. The system discussed in this paper has more measurement sensors than system states and therefore the sensors give overlapping, low-grade information affected by noise, bias, drift, etc. The dynamics of the robot and sensor system are non-linear. Therefore an AEKF is used to estimate these overlapping low-grade measured sensor data and give the best possible estimate of the mobile robot position. The adaptive mechanism in this case uses the Riccati Equation adaption. The basic idea is to change the Kalman Gain. This is done by changing the Process noise co-variance matrix adaptively. Simulations show an improved performance in the estimates from the AEKF when compared to the EKF.
Starting Page	608
Ending Page	612
File Size	453604
Page Count	5
File Format	PDF
ISBN	9810483643
DOI	10.1109/ICARCV.2002.1238493
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-12-02
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Nanyang Technological Univ
Subject Keyword	Costs Navigation Mobile robots Sensor systems Robot sensing systems Riccati equations Fuses Sensor fusion Accelerometers Gyroscopes
Content Type	Text
Resource Type	Article

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