Abstract
In vehicle visual navigation, image matching algorithm is highly critical to positioning accuracy and processing efficiency. One single matching algorithm cannot satisfy all types of image features accurate acquisition, so Harris, SUSAN, FAST, SIFT, and SURF are respectively adopted to process various road images under normal lighting condition. During practical application, the appropriate algorithm can be selected based on detection rate and running time of the above algorithms. Aiming at the illumination change interference of the collected images in vehicle visual navigation, many traditional matching algorithms for illumination change are not optimal, so an image precise matching algorithm with illumination change robustness is proposed. Because image edges and detail information have lower sensitivity for illumination change, SURF feature points are optimized by image gradient based on the idea of Canny, and the bidirectional search is used to obtain precise matching points. The experimental results show that feature point detection of the algorithm remains good stability for illumination change in images, and the matching accuracy can reach more than 94%. The algorithm is not only robust to illumination change, but also ensures higher matching speed and meanwhile improves the matching accuracy significantly.
Highlights
Image matching is one of key technologies in the study of vehicle visual navigation, which its result directly affects the accuracy of vehicle positioning and navigation
The analysis shows that the SIFT has a large number of feature point bases, so it achieves a large number of matching points and higher accuracy rate, but correspondingly has large time consumption; the SURF has relatively fewer matching points and lower matching rate, but it has high processing efficiency that meets the needs of vehicle positioning
V.CONCLUSION In vehicle visual navigation, one single matching algorithm cannot satisfy all types of image features accurate acquisition
Summary
Image matching is one of key technologies in the study of vehicle visual navigation, which its result directly affects the accuracy of vehicle positioning and navigation. Yang et al proposed a new local invariant feature detection and description algorithm [5], which the descriptor was generated based on the distance and direction histograms of the gradient It had a lower feature vector dimension, which was helpful to improve the speed of image feature matching. Wang et al [15] extracted the feature points with illumination invariance by simulating the effect of illumination change on the image, and chose local intensity sequence as the descriptor. It yielded a great progress in image matching accuracy, but the matching speed was not comparable enough.
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