Parallel Acceleration of ELAS on ARM

Fast implementation of stereo depth estimation algorithms on low-cost processors is demanding both insight of the algorithmic architecture and efficient parallelization techniques. This paper proposes an ARM-base OpenMP parallel implementation method of ELAS (Efficient Large-Scale Stereo Algorithms), which is very promising for fast computation on embedded systems. Our experiments were conducted under multi-core environments, both on an 8-core computer and an 8-core ARM ODROID-XU4. The parallel performance was examined in two ways: single image's parallel processing and batch processing of an image set containing seven pictures. Experimental results demonstrate the efficiency of our acceleration method, which can achieve a speeding-up for more than four times compared with the original sequential implementation.