Graph Based Global Robot

Graph-based global robot localization leverages graph representations of environments to achieve robust and accurate robot positioning, often integrating diverse sensor data like LiDAR, IMU, and GNSS. Current research emphasizes fusing these sensor inputs within graph optimization frameworks, such as factor graphs, to handle uncertainty and improve localization accuracy, particularly in large-scale or dynamic environments. This approach is significantly improving robot navigation capabilities, particularly in indoor settings where prior map information (e.g., from building plans) can be incorporated to enhance performance and robustness compared to traditional methods. The resulting advancements have implications for autonomous vehicles, indoor service robots, and other applications requiring precise and reliable localization.