computer vision

Experiment

Completed

Personal experiment

ArUco Marker Distance Measurement

A computer-vision experiment measuring distance to ArUco markers with camera calibration and pose estimation.

Problem

Estimate the distance from a camera to an ArUco marker in a live video feed using calibration data.

Context

An exploratory computer-vision project published as open source under the MIT license; no production deployment or validated accuracy claim.

Constraints

  • A one-time calibration step with the checkerboard images is required before any measurement.
  • Distance estimates are only valid for the specific marker the pose estimation is configured for.

Decisions

  • Calibrate the camera once with a custom checkerboard pattern and persist the camera matrix and distortion coefficients, rather than recalibrating on every run.
  • Use OpenCV's ArUco detection and pose estimation to compute the camera-to-marker distance from the calibrated parameters.

Outcomes

  • Working demonstration video in the repository showing a live distance readout; no accuracy figures are claimed.

The repository has two parts: a calibration script that processes checkerboard images with OpenCV’s camera calibration and saves the parameters, and a main script that loads them, detects the marker in the camera feed, estimates its pose, and displays the computed distance. It is an experiment built with Python, OpenCV, and NumPy - an exploration of the technique, not a measurement product.