High-Precision Mobile Robot Localization Using the Integration of RAR and AKF

The high-precision indoor positioning technology has gradually become one of the research hotspots in indoor mobile robots. Re-lax and Recover (RAR) is an indoor positioning algorithm using distance observations. The algorithm restores the robot's trajectory through curve fitting and does not require time synchronization of observations. The po-sitioning can be successful with few observations. However, the algorithm has the disadvantages of poor resistance to gross errors and cannot be used for real-time positioning. In this paper, while retaining the advantages of the original algorithm, the RAR algorithm is improved with the adaptive Kalman filter (AKF) based on the innovation sequence to improve the anti -gross error performance of the original algorithm. The improved algorithm can be used for real-time navigation and positioning. The experimental val-idation found that the improved algorithm has a significant improvement in accuracy when compared to the original RAR. When comparing to the ex-tended Kalman filter (EKF), the accuracy is also increased by 12.5%, which can be used for high-precision positioning of indoor mobile robots.