Pose Estimation

Table of contents

1. Common inputs
2. Typical pipeline
3. Why it matters in manipulation

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Pose estimation infers 3D position and orientation from sensor observations.

Common inputs

• RGB images
• RGB-D images
• Point clouds
• Fiducial markers

Typical pipeline

1. Detect keypoints or object regions.
2. Match features with model priors.
3. Solve for 6D pose.
4. Refine and track over time.

Why it matters in manipulation

• Enables closed-loop grasping
• Supports hand-eye coordination
• Improves robustness to scene changes