- Traditional localization techniques rely on triangulation or trilateration, where in a set of three or more stationary known locations is used to estimate a “client” position. For inertial navigation, these techniques can estimate client positions merely using the measured data from tri-axial accelerometers and gyroscopes. However, the use of double integration of the accelerometer data and a single integration of the gyroscope data to determine the client's position are greatly affected by gyro-drift and other noise, causing small errors to explode towards infinity. This process could be improved by applying reasonable algorithms and even deep learning tools such as the Kalman filter and simultaneous localization and mapping. Therefore, this research aims at exploring the possibility of indoor navigation based on inertial sensors that uses the fusion algorithms. Meanwhile, a multitude of formulas involving navigation are refined and corrected. These equations and optimized results provide accurate raw data for future deep learning models.