kamerakalibrointi

Kamerakalibrointi, also known as camera calibration, is the process of determining the intrinsic and extrinsic parameters of a digital camera. Intrinsic parameters describe the internal characteristics of the lens and sensor, such as focal length, principal point, and skew. Extrinsic parameters specify the camera’s rotation and translation relative to a known world coordinate system. In addition, the method estimates lens distortion coefficients that model radial and tangential deviations from the ideal pinhole model.

Accurate calibration is essential for any computer‑vision application that relies on geometrical measurements. Without it, depth estimation, 3D reconstruction, augmented reality overlay, and stereo matching will produce incorrect results. Calibration also improves the precision of photogrammetry and robotics tasks such as pose estimation, SLAM, and autonomous navigation.

Typical calibration pipelines begin by photographing a planar calibration target—often a checkerboard or a board with a known arrangement of circles or ArUco markers—in multiple orientations and positions. Feature corners or marker centres are detected in each image and matched to their known 3D coordinates. Using these correspondences, algorithms such as Zhang’s method solve for camera matrices, distortion terms, and, if required, pose of each image. The solution is optimized by minimizing reprojection error across all images.

Commonly employed targets include the classic checkerboard, Braille‑type patterns, and the ChArUco board that combines a chessboard with ArUco markers. Calibration software libraries such as OpenCV, MATLAB’s Camera Calibrator, and the ROS camera_calibration package implement these algorithms and provide graphical interfaces for selecting images and visualizing reprojection errors. Users can export calibration data in YAML or XML files for use in downstream software.

The resulting calibration data is stored and reused to undistort new images, convert pixel coordinates to real‑world units, and compute 3D positions from 2D image points. Regular recalibration may be necessary when lenses are swapped, cameras are repaired, or environmental conditions change, ensuring sustained accuracy in precision imaging tasks.