With the rapid development of smart city, the indoor positioning services became more and more important. During the existing solutions, inertial measurement unit (IMU) with pedestrian dead reckoning (PDR) was a promising scheme since they did not require external equipment in the environment. However, the orientation drift of low-cost IMU limited their application in practical. To address this problem, a zero-velocity update (ZUPT) framework included Kalman filter and particle filter is designed based on the foot-based low-cost IMU and digital floor plan to provide the service of personal navigation. In the designed Smoothing for ZUPT-aided INSs framework, the Kalman filter is used to estimate the position and attitude by zero velocity correction technique. Then, the particle filter is used to improve the localization and heading accuracy by map matching. The position estimation presented in this study achieves an average position error of 1.16 m. The experimental results show that the designed framework can solve the personal navigation problem in the case of building plan information assistance and help improve the accuracy and reliability of continuous position determination of personal navigation systems effectively.