Abstract
In order to meet the requirements of positioning accuracy of indoor mobile robot navigation system, this paper merges vision and inertial navigation system (INS) to improve robot positioning accuracy. Aiming at the low frequency of the visual navigation system (VNS) and the high frequency of the INS, a multi-frequency Kalman filter algorithm is proposed, and two different measurement equations are designed. The measurement Eq. (1) updates the position information of the inertial navigation after sampling in the INS, and the measurement Eq. (2) updates the position deviation of the mobile robot after sampling in the VNS. Finally, the accurate position information of the mobile robot is estimated by the inertial navigation position updated by the measurement equation one minus the optimal error updated by the measurement equation two. Realize the effective fusion of the location information of visual matching estimation and INS location information. The experimental results show that the multi-frequency Kalman filter algorithm is improved on the basis of the traditional filtering method, and the integrated navigation method proposed in this paper further improves the positioning accuracy of the INS.
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