An adaptively weighted targetless rotation axis calibration method based on angle prior

In rotary-stage structured-light 3D measurement systems, the calibration accuracy of the rotation axis determines the registration quality of multi-view point clouds. Among existing calibration methods, traditional target-based approaches are susceptible to the defocusing interference of edge corners during large angle rotations, while standard sphere or plane fitting methods exhibit poor robustness against noise due to the absence of rotational pose constraints. To address these issues, this paper proposes an adaptively weighted targetless rotation axis calibration method based on angle prior. This method directly extracts point cloud data from the natural surface of the measured object to establish cross-view matched point sets. Concurrently, an adaptive weighting optimization mechanism is constructed based on the rigid body transformation constraints during rotation, thereby overcoming the interference of noise within the matched point sets on axis fitting. Experimental results demonstrate that, compared with traditional target-based and standard feature fitting methods, the proposed method enhances both the calibration accuracy and noise robustness of the rotation axis.