Pose UnceRtainty

Pose uncertainty, the quantification and handling of inaccuracies in estimating an object's 3D position and orientation, is a critical challenge in robotics and computer vision. Current research focuses on developing robust methods for estimating pose uncertainty, employing techniques like Partially Observable Markov Decision Processes (POMDPs), score-based diffusion models on SE(3), and probabilistic Perspective-n-Point (PnP) approaches, often incorporating contact information or multi-view consistency to improve accuracy. Addressing pose uncertainty is crucial for reliable robot manipulation, autonomous navigation, and accurate 3D scene reconstruction, enabling safer and more effective applications in various fields.