Indoor positioning is fundamental to many smartphone applications, attracting a great deal of research efforts in recent years. Among them, RSSI (received signal strength indication) based fingerprinting has become an increasingly popular technique for realizing indoor smartphone positioning using existing WiFi infrastructures. However, wireless signal transmission is easily affected by the environment, which may result in the deviation of WiFi signal fingerprint positioning. To address this issue, multi-sensors assisted WiFi fingerprinting method is proposed to improve the performance of RSSI fingerprinting. In this method, the data obtained from the smartphone's built-in sensors like accelerometer and gyroscopeis is utilized to estimate user's trajectory along with its credibility. Then a probability model which combines the RSSI fingerprint and users' trajectory is established to implement a match between fingerprint and location. Experiments show that compared with the classical fingerprint-matching algorithm, the proposed method can effectively reduce the adverse effects of environmental changes on positioning and improve average localization accuracy by making use of the sensor data.