Aiming at the problem that the ORB feature description algorithm does not solve the scale invariance, a feature point matching algorithm combing BRISK feature descriptor with scale invariance and ORB feature detector is proposed. Firstly, the feature points in the image to be measured are detected by ORB feature detectors with scale invariance. Secondly, uniform sampling for detected feature points is conducted by referencing BRISK feature points description algorithm. Finally, the method of BF matching is used to calculate the Hamming distance for feature matching. The experimental results show that the improved algorithm effectively solves the problem that ORB feature description algorithm does not have scale invariance. By comparing with original ORB algorithm, the scale invariance of the improved algorithm is effectively improved and becomes very stable and reliable, simultaneously, the realtime performance of the improved algorithm is better than the original algorithm. It is suitable for the applications that require high realtime and large scale changes.