A Weight Adaptive Kalman Filter Localization Method Based on UWB and Odometry

Abstract

At present, the indoor positioning methods applied to robots are still subject to many limitations, which restrict the further development of indoor robots. Among the existing localization methods, ultra-wideband (UWB) technology can realize low-cost centimeter-level localization, but the localization accuracy is easily affected by Non-Line-Of-Sight (NLOS). However, using an odometry for position measurement is less susceptible to external environment interference, but it has accumulated errors. In this paper, a Weighted Adaptive Kalman Filter (WAKF) positioning method based on UWB and odometry is proposed, which eliminates the influence of the cumulative error of the odometry on the positioning accuracy to a certain extent, and solves the problem of NLOS positioning error caused by indoor obstacles, to realize the positioning of the robot in complex indoor scenes. The method uses the Kalman Filtering(KF) algorithm to fuse UWB data and odometry data, uses the power difference to identify Line-Of-Sight(LOS) and NLOS scenes, and adaptively adjusts the Kalman filter weights. The method is tested on the Epidemic Prevention and Disinfection Robot (EPADR). The experimental results show that this method meets the demand of indoor positioning, can eliminate the influence of the cumulative error of the odometry and effectively improve the positioning accuracy of the robot in the NLOS scene. Its average positioning error is 0. 16m. This method improves the positioning accuracy and robustness of the robot in the indoor environment.