Ultra-wideband(UWB)-based simultaneous localization and mapping(SLAM) researches has been widely studied to replace situations where vision-based sensors or global positioning system(GPS) signals are not available. In this paper, we propose an extended Kalman filter(EKF) SLAM framework for estimating position and orientation of a robot and UWB anchors. The framework consists of three main processes: initialization of position of a UWB anchor, pre-integration of an inertial measurement unit(IMU) sensor measurement, and the EKF framework. A state vector of the system augmented with bias of a gyroscope, scale factor of a gyroscope, and bias of an accelerometer is estimated from the UWB-IMU tightly-coupled EKF framework. With the performance test for positioning of a robot when UWB anchors are fixed or moved, it is verified that position root-mean-square-error(RMSE) is less than 20 cm.