Robust Graph Slam In Dynamic Environments With Moving Landmarks

Recent developments in human-robot interaction brings about higher requirements for robot navigation. Existing Simultaneous Localization and Mapping (SLAM) algorithms face open challenges for navigation in complex dynamic environments due to presumptions of static environments or exceeding computational limitations. In this paper, we propose a robust graph SLAM formuation exploring Expectation Maximization algorithms to characterize landmark mobility while establishing the estimations of robot trajectory and the map. We evaluate the performance of existing robust SLAM algorithms as baselines, and validate the improvement of our new framework against datasets of dynamic environments with moving landmarks.