The scale invariant feature transform(SIFT) algorithm has the problem of computational inefficiency and
mismatch. Therefor, a prior information constrained SIFT matching algorithm is proposed for the visual simultaneous
localization and mapping(vSLAM) applications. Firstly, the scale space is predicted according to the relative distance from
the robot to the feature. Then the feature position is estimated according to the state of both the robot and the feature. Finally,
sift matching is conducted within the predicted image region. The experiment results show that the proposed algorithm can
achieve better computational efficiency and matching performance.