Embedded real-time stereo estimation via Semi-Global Matching on the GPU
This enables efficient depth computation for embedded applications like autonomous vehicles, though it is incremental as it adapts an existing method to new hardware.
The paper tackled the problem of real-time dense stereo depth estimation for robotics and autonomous systems by implementing Semi-Global Matching on embedded GPUs, achieving 42 fps on a Tegra X1 for 640x480 images with 128 disparity levels.
Dense, robust and real-time computation of depth information from stereo-camera systems is a computationally demanding requirement for robotics, advanced driver assistance systems (ADAS) and autonomous vehicles. Semi-Global Matching (SGM) is a widely used algorithm that propagates consistency constraints along several paths across the image. This work presents a real-time system producing reliable disparity estimation results on the new embedded energy-efficient GPU devices. Our design runs on a Tegra X1 at 42 frames per second (fps) for an image size of 640x480, 128 disparity levels, and using 4 path directions for the SGM method.