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This paper presents a fast, online, and incremental solution for an appearance-based loop closure detection problem in an indoor environment. This problem is important in terms of the navigation of mobile robots. Appearance-based Simultaneous Localization And Mapping (SLAM) for a highly dynamic environment, called Position Invariant Robust Feature Navigation (PIRF-Nav), was first proposed by Kawewong et al. in 2010. Their results showed major improvements from other state-of-the-art methods. However, the computational expense of PIRF-Nav is beyond real time, and it consumes a tremendous amount of memory. These two factors hinder the use of PIRF-Nav for mobile robot applications. This study proposed (i) modified PIRF extraction that makes the system more suitable for an indoor environment and (ii) new dictionary management that can eliminate redundant searching and conserve memory consumption. According to the results, our proposed method can finish tasks up to 12 times faster than PIRF-Nav, with only slight percentage decline in a recall, while the precision remains 1. In addition, for a more challenging task, we collected additional data from a crowded university canteen during lunch time. Even in this cluttered environment, our proposed method performs better with real-time processing compared with other methods.