Modelbased Dynamic Pose Graph SLAM in Unstructured Dynamic Environments Navigation in dynamic environments is a challenge for autonomous vehicles operating without prior maps or global position references. This poses high risk to vehicles that perform scientific studies and monitoring missions in marine Arctic environments characterized by slowly moving sea ice with few truly static landmarks. Whereas mature simultaneous localization and mapping (SLAM) approaches assume a static environment, this work extends pose graph SLAM to spatiotemporally evolving environments. A novel modelbased dynamic factor is proposed to capture a landmarku0027s state transition model  whether the state be kinematic, appearance or otherwise. The structure of the state transition model is assumed to be known a priori, while the parameters are estimated online. Expectation maximization is used to avoid adding variables to the graph. Proofofconcept results are shown in small and mediumscale simulation, and smallscale laboratory environments for a small quadrotor. Preliminary laboratory validation results shows the effect of mechanical limitations of the quadrotor platform and increased uncertainties associated with the modelbased dynamic factors on the SLAM estimate. Simulation results are encouraging for the application of modelbased dynamic factors to dynamic landmarks with a constantvelocity kinematic model.

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.