Robot indoor location modeling and simulation based on Kalman filtering

Abstract

Wireless signal fingerprint positioning technology has been widely used in indoor positioning. In view of the influence of a large number of interference noise in indoor, the error of receive signal strength indicator is large, the more complex and chaotic indoor environment, the location accuracy deviation of the system will be very large; an algorithm based on Kalman filter is proposed to filter the velocity and direction of motion of indoor robots. The position coordinates of the robot are estimated by RSSI-based positioning method, and the indoor robot positioning model and Kalman filter model are established. Kalman filter autoregressive algorithm is used to optimize the estimated position coordinates of the robot. Mathematical reasoning and simulation results show that the probability of positioning error is 80% when Kalman filter is not used, and the location error is controlled within 1.2 m after Kalman filter, which effectively improves the location accuracy of indoor robots.