FloW Vision: Depth Image Enhancement by Combining Stereo RGB-Depth Sensor

Human can recognize an object just by looking at the environment, this capability is very useful for designing the reference of humanoid robot with the ability of adapting it on its environment. By knowing the field conditions that exist in such environments, robot can understand the obstacles or anything that can be passed. To do that, robot vision needs to have a knowledge to understanding an obstacles that exist around it. We investigate possible improvements that can be achieved in depth estimation by merging coded apertures and stereo cameras. The demonstrated results of this analysis are encouraging in the sense that coded apertures can provide valuable complementary information to stereo vision based depth estimation in some cases. We show that with this system, it is possible to extract depth information robustly, by utilizing the inherent relation between the disparity and defocus cues, even for scene regions which are problematic for stereo matching.