Simultaneous vision system calibration and full-motion estimation using a sequence of noisy images from a stereo affine cameras

The paper describes a kinematic model-based solution to estimate simultaneously the calibration parameters of the vision system and the full-motion of an object using a sequence of noisy images captured by a set of stereo affine cameras. Assuming a smooth motion, an Iterated Extended Kalman Filter (IEKF) is used to recursively estimate the cameras projection matrices and the object's full-motion over time. The estimator was developed having in mind the structure health monitoring of large structures of civil engineering domain, observed at long distance, in particular, of long deck suspension bridges. Results related to the performance evaluation, obtained by numerical simulation and with real experiments, are reported.