  In view of contemporary panoramic camera-laser scanner system, the
traditional calibration method is not suitable for panoramic cameras whose
imaging model is extremely nonlinear. The method based on statistical
optimization has the disadvantage that the requirement of the number of laser
scanner's channels is relatively high. Calibration equipments with extreme
accuracy for panoramic camera-laser scanner system are costly. Facing all these
in the calibration of panoramic camera-laser scanner system, a method based on
supervised learning is proposed. Firstly, corresponding feature points of
panoramic images and point clouds are gained to generate the training dataset
by designing a round calibration object. Furthermore, the traditional
calibration problem is transformed into a multiple nonlinear regression
optimization problem by designing a supervised learning network with
preprocessing of the panoramic imaging model. Back propagation algorithm is
utilized to regress the rotation and translation matrix with high accuracy.
Experimental results show that this method can quickly regress the calibration
parameters and the accuracy is better than the traditional calibration method
and the method based on statistical optimization. The calibration accuracy of
this method is really high, and it is more highly-automated.
