Traditional image feature points matching algorithms of large amounts of data bring large computation burdens

particularly for real-time systems

such as visual odometry

or low-power devices

such as cellphones. This condition has led to an intensive search for replacements with lower computation cost. This paper proposes a robust real-time feature-point matching algorithm designated RRM. It first determines the edge area of the image by deviation operation

and then finds the anchor point or the gradient local maximum points

which are likely to be the feature points in the edge area. Next

it determines the direction of feature points by calculating Intensity Centroid

and then describes the feature points based on the improved Brief. Finally

it matches the feature points by combining Hamming distance with symmetrical match. Compared with a variety of feature-point matching algorithms

the proposed algorithm attains a higher success rate of 83% for images with complex backgrounds such as illumination changes

viewpoint changes

and scaling and rotation changes. The proposed algorithm is superior to and more stable than others. Experimental results indicate that the proposed algorithm solves the limitation of traditional feature-point matching effectively

without losing accuracy. The proposed algorithm can be used for image stitching

object tracking

and object recognition.