Calibration of vision systems based on pseudo-random patterns

Solving visual features' correspondence and dealing with missing data are two factors of limitations for points registration techniques. To tackle this problem, we conceived a pattern, primarily designed for structured lighting vision systems, which may also be used for camera calibration purposes. The pattern design previously presented in [1] provides a huge of benefits. Among them, we firstly present a new calibration technique of a structured lighting system and secondly an automatic distortion compensation based on a printed pattern. These two well-known issues are very useful in 3D vision-based metrology with range data, for instance for model-based visual robot control, especially when the model is incrementally built with a real-time 3D reconstruction of moving surfaces. Perhaps, one of the most significant profit with a high Hamming distance pattern is the ability to reliably decode its projected individual elements even if several of items are missing, as it greatly extends the range of measurements volume. A technique which solves the distortion parameters by means of a robust M-estimator algorithm is presented. It uses a printed pattern and it allows the distortion be corrected with a single view and without the computation of other (intrinsic/extrinsic) parameters, even in presence of occlusions. Experimental results, in one hand by means of a printed pattern for the distortion compensation of a rigid endoscope and on the other hand by means of a projected pattern for the calibration of the structured lighting system, show very good performance for the 3-D reconstruction.