Wearable sensors with an inertial measurement unit (IMU) are popular for indoor positioning and activity pattern detection. The IMUs can be connected to a wireless transmission module, allowing users to monitor and process motion-related parameters remotely. Because of the complexity and uncertainty of signals in indoor environments, a radio frequency (RF) positioning system alone is often insufficient to provide the position accuracy and stability required for many applications, for example, position-guided indoor mobile robots. Our idea is to fuse two or more sources of data to generate highly accurate positioning information. Specifically, we have developed an IMU-aided RF positioning system, aiming to improve the accuracy of the system in indoor environments for mobile robotics. This approach combines the measurements of the accelerometers, gyroscopes, and magnetometer from an IMU via a complementary filter. The work includes a development of a calibration algorithm, which reduces the IMU drift and error. With the calibrated data, the trapezoidal integration method can now better use the accelerometer data to estimate the velocity and displacement of the mobile robot. In order to transform the data in the coordinate system of the IMU mounted on the mobile robot body to the ground positioning coordinate that RF positioning uses, we implement a quaternion rotation algorithm. This enables the fusion of the IMU and RF positioning estimates to accurately determine the moving trajectory of the mobile robot and guide its moving directions.