Extending two non-parametric transforms for FPGA based stereo matching using bayer filtered cameras

Stereo vision has become a very interesting sensing technology for robotic platforms. It offers various advantages, but the drawback is a very high algorithmic effort. Due to the aptitude of certain non-parametric techniques for Field Programmable Gate Array (FPGA) based stereo matching, these algorithms can be implemented in highly parallel design while offering adequate real-time behavior. To enable the provision of color images by the stereo sensor for object classification tasks, we propose a technique for extending the rank and the census transform for increased robustness on gray scaled bayer patterned images. Furthermore, we analyze the extended and the original algorithms’ behavior on image sets created in controlled environments as well as on real world images and compare their resource usage when implemented on our FPGA based stereo matching architecture.