The closed-loop detection method for SLAM (Simultaneous Localization and Mapping) is prone to perceptual deviation in complex scenes with multiple ambiguities.Based on the closed-loop probability model,a closed-loop detection method that combines local SURF features with global ORB features is proposed.Firstly,robust SURF feature and global ORB feature are used to describe the image locally and globally.Secondly,the discrete Bayesian closed-loop probability model for multi-feature scene description is constructed,and the observation likelihood probability is constructed for multi-feature spaces,in which the local feature space calculates the observation likelihood probability based on the bag-of-words model,and the global feature space calculates the observation likelihood probability based on KNN nearest neighbor method.Finally,considering the temporal consistency of images,a multi-step,closed-loop candidate frame extraction method is designed based on epipolar constraints to further reduce the perception deviation.The experimental results show that the algorithm can eliminate most of the false-positive matching cases in multi-ambiguity scenes,and has better closed-loop detection effect and higher closed-loop accuracy compared with FAB-MAP2.0 and BoW methods.