Environmental target image recognition of coal mine rescue robot

In view of problems that traditional scale-invariant feature transform（SIFT） algorithm has calculation redundancy in searching key points of vision images and poor real-time performance in target recognition, an improved SIFT algorithm was proposed which was applied to coal mine rescue robot to realize environmental information perception and target recognition matching. The improved SIFT algorithm adopts Mahalanobis distance to replace Euclidean distance in the traditional SIFT algorithm and simplifies extraction of the image feature points, avoids mismatching of feature point. The field test results show that the improved SIFT algorithm improves real-time performance in underground environmental target recognition and accuracy of target matching of coal mine rescue robot, which makes visual premise for realizing obstacle-avoidance and walking of coal mine rescue autonomous robot.