Robot Positioning Technology Based on the Fusion of Two-dimensional Code Positioning and Inertial Navigation
In the research of bed and chair integrated robots,the traditional multi-sensor fusion localization algorithm cannot effectively deal with time-varying noise,resulting in low localization accuracy.Based on the research of QR code and IMU inertial navigation fusion positioning,an adaptive fuzzy inference unscented Kalman filter algorithm is proposed,which could adjust the noise of unscented Kalman filter in real time to realize accurate positioning of the bed and chair module.The results from sensor data simulation and robot car experiment show that the proposed algorithm can still obtain the optimal positioning results when the sensor positioning error is large.Compared with the traditional unscented Kalman filter algorithm,the mean square error is reduced by 38.94％,and the maximum error is reduced by 21.6％.
multi-sensor fusiongwo-dimensional code positioningunscented kalman filterfuzzy inference system
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