Pose graph optimization is a common method for solving Simultaneous Localization and Mapping (SLAM) problems in robotics.
The general idea is to represent the mapping and localization information as a graph of positions and orientations and then create constraints between those poses.
Through optimization, we minimize errors in the constraints and achieve a globally consistent estimate of the poses and the surroundings. This may be visualized as a type of “tension” created between nodes (poses) and the distance they have from their measured points.
