Hand-Eye Calibration for Robot Vision with RF-DETR

Hand-eye calibration enables robots to translate camera-detected object positions into coordinates the robot can reach by measuring the fixed spatial relationship between a wrist-mounted camera and gripper in an eye-in-hand configuration. The process calculates a camera-to-gripper transformation using checkerboard observations at multiple robot poses, OpenCV’s solvePnP and calibrateHandEye functions, and combines it at runtime with the robot’s current base-to-gripper pose. In the described Roboflow Workflow, RF-DETR Nano detects a target object, the center of its bounding box is converted from image pixels into a 3D camera-frame point using camera intrinsics and depth data, and matrix transformations produce the target’s robot-base position for grasping or motion planning. The example uses fixed mock depth and transform values to validate workflow calculations, while a real deployment would require RGB-D depth, calibrated camera parameters, and live robot pose data. Recalibration is required when the camera mount or gripper reference changes, whereas detection models need updating only when the visual recognition task changes.

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