Thesis for the degree of Master of Science Online Stereo Calibration using FPGAs

Stereo vision is something that most people do everyday without even realizing it. More specifically, as we walk around our environment, our two eyes are constantly taking in a pair of images of the world. Our brain then fuses these two images and unconsciously computes the approximate depth of the objects you see around you. In the field of computer vision, our two eyes are replaced by two cameras and our brain is replaced by a computer. However, the aim of stereopsis remains the same. That is, given a pair of stereo images, we want to compute the scene depth. Now, with human vision, in order to calculate scene depth, your brain probably needs to know how far apart your eyes are and what orientations your eyes are in. Similarly, with computer vision, you need to know the translation and rotation between the cameras before you can calculate scene depth. Determining the translation and rotation is known as stereo calibration. This thesis deals with the problem of stereo calibration. More specifically, given a continuous stream of image pairs, we want to perform stereo calibration in real time. However, due to the computational intensity of these calibration calculations, today’s computers are not yet fast enough to do this in real time. In order to solve this problem, we suggest using programmable logic, i.e. Field Programmable Gate Arrays (FPGAs), for parts of the calibration process. The reason for using programmable logic is that many of the steps needed in the stereo calibration algorithm can be done independently of each other. This means that a parallel approach, such as using FPGAs, will have a clear advantage. By implementing steps in the algorithm in parallel on FPGAs, we can reduce the computational time needed per frame and achieve real time performance. The resulting stereo calibration that was achieved in this thesis is very accurate. For example, we can determine the verge angle between the two cameras with an accuracy of less than one degree with the system running at speeds exceeding the frame rate needed for real time performance. Hence, this thesis clearly shows the advantage of using FPGAs to solve computationally intensive computer vision tasks.