GPU-enhanced Multimodal Dense Matching

Multiple modalities for stereo matching are beneficial for robust path estimation and actioning of autonomous robots in harsh environments, e.g. in the presence of smoke and dust. In order to combine the information resulting from the different modalities, a dense stereo matching approach based on semi-global matching and a combined cost function using cross-based support regions and phase congruency shows a good performance. However, these computationally complex algorithmic steps set high requirements for the mobile processing platform and prohibit a real-time execution at limited power budget on mobile platforms. Therefore, this paper explores the usage of graphic processors for the parallelization and acceleration of the aforementioned algorithm. The resulting implementation performs the computation of phase congruency and cross-based support regions at 68 and 5 frames per second for [960 × 560] pixel images on a Nvidia Quadro P5000 and Tegra X2 GPU respectively.