Online self-calibration for mobile vision based on laser imaging and computer algorithms

A self-calibration technique for mobile three-dimensional vision is presented. This technique determines the vision parameters during the vision task based on computer algorithms and image processing. The three-dimensional vision is performed by a Bezier network based on laser line projection. This network provides the data to perform the online self-calibration when the vision system is modified. Here, the changes of the extrinsic and intrinsic parameters are determined. The structure of the network is performed by the line shifting provided by the surface depth. From this structure, the data for the initial calibration and online self-calibration are deduced. In this manner, the calibrated references and physical measurements are avoided to perform the online self-calibration. Therefore, calibration limitations caused by online modifications are overcome to perform the mobile vision. Thus, the proposed self-calibration improves the accuracy and performance of the mobile vision. It is because online data of calibrated references are not passed to the vision system. This procedure represents a contribution in the field of the online recalibration, which is performed based on calibrated references. To elucidate this contribution, an evaluation is performed based on the self-calibration methods, which are reported in the recent years. Also, the time processing is described.