Accelerated catadioptric omnidirectional view image unwrapping processing using GPU parallelisation

Catadioptric omnidirectional view sensors have found increasing adoption in various robotic and surveillance applications due to their 360° field of view. However, the inherent distortion caused by the sensors prevents their direct utilisations using existing image processing techniques developed for perspective images. Therefore, a correction processing known as “unwrapping” is commonly performed. However, the unwrapping process incurs additional computational loads on central processing units. In this paper, a method to reduce this burden in the computation is investigated by exploiting the parallelism of graphical processing units (GPUs) based on the Compute Unified Device Architecture (CUDA). More specifically, we first introduce a general approach of parallelisation to the said process. Then, a series of adaptations to the CUDA platform is proposed to enable an optimised usage of the hardware platform. Finally, the performances of the unwrapping function were evaluated on a high-end and low-end GPU to demonstrate the effectiveness of the parallelisation approach.