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HALOWERK OpenCV Robot Hand-Eye Calibration
x402
HALOWERK OpenCV Robot Hand-Eye Calibration is a paid API for AI agents from medien.halowerk.com, paid per call via x402, $0.003/call, status unknown (last checked 2026-10-30).
Computes the rigid camera-to-gripper transform from synchronized gripper-to-base and target-to-camera pose pairs using OpenCV hand-eye calibration
Price / call$0.003
Success—
StatusUnknown
How this service works
Solves the hand-eye problem: given a series of gripper-to-base poses read from the robot and the matching target-to-camera poses seen by the camera, it recovers the rigid transform between camera and gripper that stays constant as the arm moves. This is what lets a robot act on what its camera sees. The poses must be genuinely synchronized — a pairing that is off by one sample produces a confident and entirely wrong transform, and nothing in the data reveals it.
Output
Returns the recovered 4x4 rigid transformation matrix representing the constant camera-to-gripper (or gripper-to-camera) transform, enabling the robot to map camera observations into the robot's coordinate frame.
Example use cases
Eye-in-hand pick-and-place setup
“I'm setting up a robot arm with a wrist-mounted camera for pick-and-place. I've recorded 8 synchronized gripper-to-base and target-to-camera 4x4 pose matrices while moving the arm. Can you calibrate and give me the camera-to-gripper transform so the robot knows where the camera is?”
Verify new camera mount alignment
“We just remounted the camera on our robot gripper and need to recheck the hand-eye transform. Here are 12 gripper-to-base poses and 12 matching target-to-camera poses from our calibration run — please compute the updated camera-to-gripper rigid transform.”
Automated calibration pipeline for robot fleet
“I'm building an automated calibration workflow for multiple robot arms. For each robot I'll pass in a set of at least 5 gripper-to-base and target-to-camera 4x4 transformation matrices — run the hand-eye calibration and return the camera-to-gripper transform for each one.”
Technical details
Endpoint
POSThttps://medien.halowerk.com/v1/oss-opencv-robot-hand-eye-calibrate?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a fast, pay-per-call hand-eye calibration computation backed by OpenCV without managing local dependencies. It is ideal for robotics pipelines that already collect synchronized pose data and need to periodically recompute the camera-to-gripper transform, especially in cloud-deployed or agent-driven automation workflows where installing OpenCV locally is impractical.
Side effects
- Fewer than 3 pose pairs provided — minimum 3 required
- Mismatched array lengths between gripper_to_base and target_to_camera — pairs must correspond exactly
- Off-by-one synchronization error in input poses produces a plausible but entirely incorrect transform with no detectable error signal
- Non-rigid or degenerate motion between poses causes numerically unstable calibration
- Malformed 4x4 matrix (wrong dimensions) returns a validation error
- Insufficient motion diversity in the pose sequence leads to poorly conditioned solution
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| gripper_to_base | array | |
| target_to_camera | array |
Tags
x402
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