Graph-based Simultaneous Localization and Mapping with incorporated dynamic object motion

Over the last years, Simultaneous Localization and Mapping (SLAM) in dynamic environments has received more attention. This paper presents a SLAM algorithm in which dynamic object information is included within the graph-based optimization approach. By exploiting knowledge about the object's motion within the scene, the constructed map is a more accurate representation of the environment. Using data from simulation, we show that the robot's trajectory and the dynamic object's trajectory better aligns with respect to the ground truth. Real-world experiments, which includes human motion within the optimization, show that the robot's trajectory and thus the environment map is improved. This is verified based on the comparison between the constructed maps with and without the incorporation of the human motion. The validity of the map is obtained by evaluating three metrics from literature and a comparison to the building plans of the environment.