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.

翻译：在视觉SLAM中，实现精确特征匹配需要消耗大量时间，严重影响了系统的实时性。本文提出了一种通过融合GMS（基于网格的运动统计）与RANSAC（随机采样一致性）来加速视觉SLAM并剔除误匹配特征的方法。该方法首先利用GMS算法估计邻域内的匹配对数量，并基于置信度对匹配结果进行排序。随后，采用随机采样一致性（RANSAC）算法进一步剔除误匹配特征。为解决随机选取所有匹配对耗时的问题，本方法将其转化为优先从高置信度匹配中选取样本的问题，从而能够迭代求解最优模型。实验结果表明，在KITTI、TUM desk及TUM doll数据集上，所提方法在达到与原始GMS-RANSAC相当精度的同时，平均运行时间减少了24.13%。