In this paper, we propose a new algorithm of mapping dynamic indoor environments. Instead of accurate but expensive laser, we employ sonar and camera to map dynamic structured indoor environments. Based on fuzzy-tuned grid-based map (FTGBM), we use two methods: sonar temporal difference (STD) and statistical background subtraction (SBS), to detect and track moving objects when mapping dynamic environments. The former is a consistency-based method realized by monitoring a sequence of temporal lattice maps for a certain number of measurement periods to detect moving objects by using sonars; and the latter is a background subtraction technique which adopts an expectation maximization (EM) learned 3-class mixture of Gaussians to model the nonstationary background relied on sufficient update during mapping process. After finding the moving objects, we propose a fuzzy-tuned integration (FTI) method to incorporate the results of motion detection into the mapping process. The simulation and experiment demonstrate the capabilities of our approach.