Towards real time vision based UUV navigation using GPU technology

The last decade has witnessed the establishment of image processing as a viable means of aiding underwater navigation. However, many such systems are only implemented in pre-processing and offline due to their excessive computational demands. Real-time techniques often require special purpose hardware or impose limitations on the system to obtain real-time performance at the expense of accuracy. The rapidly improving performance of graphics hardware as well as the recent improvements in its programmability with developments such as NVIDIA's CUDA, has made graphics hardware a practical alternative to CPUs for highly parallel tasks such as image processing. This paper details the implementation of the scale invariant feature transform (SIFT) using a graphics processing unit (GPU) instead of a conventional CPU in order to achieve real-time performance of a vision based navigation system. The performance of both the GPU and CPU SIFT implementations are compared and evaluated using images gathered by the newly developed ROVLATIS off the west coast of Ireland.