A Stereo Matching Algorithm for Fast Extraction of Binocular Depth in Visual SLAM

With the wide application of artificial intelligence in intelligent driving, visual SLAM technology has become one of the popular research areas. Binocular SLAM, as one of the cheapest cost technologies in SLAM, has become a research hotspot for many scholars. For the problems of high time consumption, poor accuracy and high mis-matching rate of traditional binocular stereo matching algorithm, an adaptive weight cost aggregation D* algorithm based on Dijkstra's idea is proposed. The algorithm improves on the local stereo matching Census algorithm, and fuses the Census algorithm and grayscale histogram in the cost matching stage to make it compatible with computational efficiency and accuracy, which can achieve real-time effect when combined with hardware acceleration. The experimental results show that the improved Census algorithm greatly improves the stereo matching accuracy, and computational efficiency. In comparison with other algorithms on the Middlebury 2.0 testbed, the improved algorithm is found to be 42% more efficient and 1.2% better in terms of mis-matching rate.