Long-Term Visual Inertial SLAM based on Time Series Map Prediction

With the advance in the field of mobile robots, autonomous robots are required for long-term deployment in dynamic and complex environments. However, the performance of Visual Inertial SLAM systems in long-term operation is not satisfactory, and most long-term SLAM systems assumes periodic changes in the environment. This paper presents a novel solution for long-term monocular VI SLAM system in dynamic environment based on autoregression(AR) modeling and map prediction. Map points are first classified into static and semi-static map points according to a memory model. Modeling and prediction of the different states of semi-static map points are performed that are derived from time series models. The predicted map is then fused with the current map to achieve a better forecast for the next frame if the prediction is not satisfactory enough. Experiments are carried out on an embedded system. The results indicate that the map prediction is reliable and the proposed approach improves the performance of long-term localization and mapping in dynamic environments.