Pose Graph Optimization

Pose graph optimization (PGO) refines estimates of robot poses (position and orientation) from relative measurements, crucial for simultaneous localization and mapping (SLAM). Current research emphasizes robust solutions to outlier measurements, exploring methods like incremental probabilistic consensus and graduated non-convexity to improve accuracy and efficiency, often within Riemannian optimization frameworks or leveraging graph neural networks. These advancements are vital for improving the accuracy and reliability of robotic navigation, autonomous driving, and other applications relying on precise pose estimation in challenging environments.