Abstract: A predictive erroneous matching risk minimization based feature selection method is proposed for visual SLAM (simultaneous localization and mapping).It uses predictive erroneous matching risk to measure the influence of newly detected features on the oncoming feature matching process.Then,based on a multi-layer ranking method,the new features with lower erroneous matching risk and higher repeatability are selected for initialization with priority.This method can adaptively select good features that are not prone to be erroneously matched according to the uncertainty in state estimation. Therefore,the convergency and consistency of the SLAM algorithm can be ensured.The comparative experiment results on a mono-SLAM system validate that the proposed method has significant advantages over the existing methods in reducing erroneous matching rate and ensuring the correctness of state estimation.