In this paper, we propose a new area-based stereo matching method by improving the classical Census transform. It is a difficult task to match the corresponding points in two images taken by stereo cameras, mostly under variant illumination and non-ideal conditions. The classic Census nonparametric transform offers some improvements in the accuracy of disparity map in these conditions but it also has some disadvantages. Because of the complexity of the algorithm, the performance is not suitable for real-time robotic systems. In order to solve this problem, this paper presents the differential transform using Maximum intensity differences of the pixel placed in the center of a defined window and the pixel in the neighborhood to reduce complexity and obtain better performance compared to the Census transform. Experimental results show that the proposed method, achieves better efficiency in terms of speed and memory consumption. Moreover, we have added a new feature to widen the depth detection range. With the help of the proposed method, robots can detect obstacles between 25cm to 400cm from robot cameras. The result shows that the method has the ability to work in a wide variety of lighting conditions, while the stereo matching performs the depth detection computation with speed of 30FPS.
Improved census transform for noise robust stereo matching