Why
The LeRobot card is about moving the arm; this is the step in front of it, and skipping it is why a cheap arm grabs at empty air. A policy trained on demonstrations learns actions in the robot's own coordinate frame, but everything the camera reports is in pixels, and nothing in the imitation-learning loop converts between them for you. So the question this card answers is the plainest one in robotics vision — two hundred pixels on screen is how many millimetres in the world? — and the reason it belongs in this catalogue rather than in a bookmark folder is that the honest answer is a measurement, not a pipeline. It also happens to be free: a built-in webcam and a sheet of A4 are the entire bill of materials, which makes it the cheapest way to find out where the ceiling actually is before spending anything on hardware. The V in VLA starts here.
How it works
Four steps, and only the last two are optional. Intrinsics: fifteen to twenty chessboard or ChArUco shots through cv2.calibrateCamera yield the matrix K (fx, fy, cx, cy) and the distortion coefficients, and reprojection RMS is the first of the two numbers this card exists to produce — under one pixel is the pass mark. Pose: an ArUco marker on the object or the gripper gives full 6-DOF position and rotation relative to the camera, and there is one version trap worth knowing before it costs half an hour — cv2.aruco.estimatePoseSingleMarkers was deprecated in OpenCV 4.7, so the current path is cv2.aruco.ArucoDetector for corners followed by cv2.solvePnP with SOLVEPNP_IPPE_SQUARE, which is why a large share of the tutorials online no longer run as written. Hand-eye: cv2.calibrateHandEye aligns the camera frame with the robot base frame, and the classic first mistake is not the algorithm choice (Tsai, Park, Horaud) but the setup — a camera mounted on the arm is eye-in-hand, a camera on a tripod is eye-to-hand, and getting that backwards produces a transform that is wrong in a way that still looks plausible. Inference: cv2.dnn runs an ONNX detector with no second framework, which is the natural lightweight pairing for an edge board like the Jetson Orin Nano Super and the reason that item sits next in the queue. The second number is the one that actually settles what this camera can do: put the marker at a distance measured with a ruler, compare it to tvec, and record the error as a percentage. Everything above is setup for those two figures — RMS and range error — and a card that reports them is worth more than one that reports that the axes rendered.