Long-Term Localization With Time Series Map Prediction for Mobile Robots in Dynamic Environments

In many applications of mobile robot, the environment is constantly changing. How to use historical information to analysis environmental changes and generate a map corresponding with current environment is important to achieve high-precision localization. Inspired by predictive mechanism of brain, this paper presents a long-term localization approach named ArmMPU (ARMA-based Map Prediction and Update) based on time series modeling and prediction. Autoregressive moving average model (ARMA), a kind of time series modeling method, is employed for environmental map modeling and prediction, then predicted map and filtered observation are fused to fix the prediction error. The simulation and experiment results show that the proposed method improves long-term localization performance in dynamic environments.