autocalibration

Autocalibration is the process by which a system estimates its own calibration parameters automatically from observations, without relying on dedicated calibration objects or external measurements. In imaging and computer vision, autocalibration commonly refers to determining a camera’s intrinsic parameters—such as focal lengths, principal point, skew, and distortion coefficients—and, for multi-view setups, aspects of the extrinsic geometry, using only captured images or sensor data.

How it works

Autocalibration typically relies on data from multiple views of a scene. By exploiting constraints from epipolar geometry and motion between views, and often within a bundle adjustment framework, algorithms solve for camera parameters that minimize reprojection error across views. Because intrinsic parameters can drift with zoom, focus, or temperature, autocalibration aims to estimate them online or from data gathered in the field. In practice, additional priors or constraints—such as zero skew, unit aspect ratio, or a fixed principal point—are used to avoid degenerate solutions and improve robustness.

Applications

Autocalibration is widely used in photography, robotics, autonomous vehicles, and 3D reconstruction to reduce or eliminate the need for handmade calibration targets. It enables cameras to adapt to changing conditions, maintain accurate camera models during operation, and support tasks such as structure-from-motion, simultaneous localization and mapping, and augmented reality. Some systems also perform multi-sensor autocalibration to align cameras with other modalities like LiDAR or radar.

Challenges

Autocalibration can suffer from degeneracies when motion or scene structure is insufficient, be sensitive to noise, and depend on diverse viewpoint and lighting changes. Online autocalibration must balance speed and accuracy, while offline methods may require large image collections and careful initialization.

Variations

The term is closely related to self-calibration and is used across computer vision, photogrammetry, and instrumentation domains.