Perception

Pose Estimation

Pose estimation is the determination of an object's or robot's position and orientation, typically the full 6-DoF pose, from sensor data. Classical pipelines match features or fit models using PnP, ICP, or template matching, while learned methods regress poses directly or via keypoints, with systems such as PoseCNN and category-level and model-free approaches like FoundationPose extending to novel objects. The term also covers human and robot body pose estimation.

Why it matters for physical AI

Grasping, insertion, and tool use all hinge on knowing object poses within task tolerances, and pose error is among the most common root causes of manipulation failure in deployed systems.