A Flexible High-Resolution Real-Time Low-Power Stereo Vision Engine

Abstract

Stereo Vision has been a focus of research for decades. In the meantime, many real-time stereo vision systems are available on low-power platforms. Several products using stereo vision exist on the market. So far, all of them are based on image sizes up to 1 MP. They either use a local correlation-like stereo engine or perform some variant of Semi-Global Matching (SGM). However, many modern cameras deliver 2 MP images (full High Definition) at framerates beyond 20 Hz. In this contribution we propose a stereo vision engine tailored for automotive and mobile applications, that is able to process 2 MP images in real-time. Note that also the disparity range has to be increased when maintaining the same field of view with higher resolution. We implement the SGM algorithm with search space reduction techniques on a reconfigurable hardware platform, yielding a low power consumption of under 1 W. The algorithm runs at 22 Hz processing 2 MP image pairs and computing disparity maps with up to 255 disparities. The conducted evaluations on the KITTI Dataset and on a challenging bad weather dataset show that full depth resolution is obtained for small disparities and robustness of the method is maintained at a fraction of the resources of a regular SGM engine.