Vision based Autonomous Vehicle Navigation with Self-Organizing Map Feature Matching Technique

Vision is becoming more and more common in applications such as localization, autonomous navigation, path finding and many other computer vision applications. This paper presents an improved technique for feature matching in the stereo images captured by the autonomous vehicle. The Scale Invariant Feature Transform (SIFT) algorithm is used to extract distinctive invariant features from images but this algorithm has a high complexity and a long computational time. In order to reduce the computation time, this paper proposes a SIFT improvement technique based on a Self-Organizing Map (SOM) to perform the matching procedure more efficiently for feature matching problems. Experimental results on real stereo images show that the proposed algorithm performs feature group matching with lower computation time than the original SIFT algorithm. The results showing improvement over the original SIFT are validated through matching examples between different pairs of stereo images. The proposed algorithm can be applied to stereo vision based autonomous vehicle navigation for obstacle avoidance, as well as many other feature matching and computer vision applications.