  In Visual SLAM, achieving accurate feature matching consumes a significant
amount of time, severely impacting the real-time performance of the system.
This paper proposes an accelerated method for Visual SLAM by integrating GMS
(Grid-based Motion Statistics) with RANSAC (Random Sample Consensus) for the
removal of mismatched features. The approach first utilizes the GMS algorithm
to estimate the quantity of matched pairs within the neighborhood and ranks the
matches based on their confidence. Subsequently, the Random Sample Consensus
(RANSAC) algorithm is employed to further eliminate mismatched features. To
address the time-consuming issue of randomly selecting all matched pairs, this
method transforms it into the problem of prioritizing sample selection from
high-confidence matches. This enables the iterative solution of the optimal
model. Experimental results demonstrate that the proposed method achieves a
comparable accuracy to the original GMS-RANSAC while reducing the average
runtime by 24.13% on the KITTI, TUM desk, and TUM doll datasets.
