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Algorithm of CUKF and Its Application to Mobile Robot Integrated Navigation


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Algorithm of CUKF and Its Application to Mobile Robot Integrated Navigation
SHI Xing-xi12ZHAO Chun-xia2GUO Jian-hui2
1.School of Sciences;2.School of Computer Science and Technology,NUST,Nanjing 210094,China
unscented Kalman filter mobile robots integrated navigation difference global positioning system dead reckoning
The algorithm about a constrained unscented Kalman filter(CUKF)is proposed to estimate the localization of the mobile robot.The unscented Kalman filter(UKF) doesn’t compute the Jacobian matrix nor the Hessian matrix when it processes the nonlinear questions and it can decrease effectively the error of nonlinear system caused by the linearization.The algorithm of CUKF takes full advantage of characteristics of the UKF and some restrictions are increased.The road direction of the digital map database in geographic information system(GIS) environment is regarded as the state constrained condition.The nonlinear optimization estimation about DGPS/DR(difference global positioning system/dead reckoning) integrated navigation system can be resolved through the Lagrangian function.Simulation results show that the CUKF can more effectively improve the position precision than the UKF.


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Last Update: 2012-11-19