Model-based
CAD or mesh matched to the point cloud or depth image. Strong when geometry is stable and models exist.
Knowledge · Robotics
Pose estimation for robot vision
Robot vision needs more than detection: the robot must know where and how the part sits in 3D space. That is 6D pose estimation.
Concept
What 6D pose means
Six degrees of freedom: translation (X, Y, Z) and rotation (roll, pitch, yaw) of the part frame relative to the camera or robot base. 2D vision gives you position and angle in the plane; bin picking needs the full pose for tilted or stacked parts.
Learning-assisted
Networks propose pose or keypoints; often combined with geometric refinement for industrial accuracy.
Symmetry handling
Cylindrical or ambiguous parts need explicit symmetry rules or the robot receives equivalent poses.
Dependencies
Calibration and models
Pose estimation output is only useful after hand-eye calibration maps camera coordinates to the robot frame. Model quality and part variation directly affect match stability.
- 01
Hand-eye transform
Without a stable camera-to-robot relation, pose error becomes grasp miss. See hand-eye calibration.
- 02
CAD / mesh fidelity
Casting flash, wear, or loose tolerances vs a nominal model reduce match score in cluttered bins.
- 03
Partial views
Occlusion limits visible surface; pose estimation must tolerate missing data or defer the pick.
- 04
Downstream grasp frame
Grasp poses are defined relative to the part; error in part pose propagates directly to the TCP target.
Pose accuracy in your cell
Share part geometry, camera setup, and grasp tolerances. We align pose estimation with calibration and bin picking workflow.