LongTerm Visual Inertial SLAM based on Time Series Map Prediction With the advance in the field of mobile robots, autonomous robots are required for longterm deployment in dynamic and complex environments. However, the performance of Visual Inertial SLAM systems in longterm operation is not satisfactory, and most longterm SLAM systems assumes periodic changes in the environment. This paper presents a novel solution for longterm monocular VI SLAM system in dynamic environment based on autoregression(AR) modeling and map prediction. Map points are first classified into static and semistatic map points according to a memory model. Modeling and prediction of the different states of semistatic 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 longterm localization and mapping in dynamic environments.

Predictive and adaptive maps for longterm visual navigation in changing environments In this paper, we compare different map management techniques for longterm visual navigation in changing environments. In this scenario, the navigation system needs to continuously update and refine its feature map in order to adapt to the environment appearance change. To achieve reliable longterm navigation, the map management techniques have to (i) select features useful for the current navigation task, (ii) remove features that are obsolete, (iii) and add new features from the current camera view to the map. We propose several map management strategies and evaluate their performance with regard to the robot localisation accuracy in longterm teachandrepeat navigation. Our experiments, performed over three months, indicate that strategies which model cyclic changes of the environment appearance and predict which features are going to be visible at a particular time and location, outperform strategies which do not explicitly model the temporal evolution of the changes.