Test Experiment Design for IMU-Based Angle Measurement Systems

Abstract

Inertial Measurement Unit (IMU) sensors are used in many applications that include aviation, vehicle systems, unmanned aircraft, indoor navigation, health, and robotic systems. An IMU consists of accelerometers and gyroscope sensors combined in a single module. However, the accelerometer or gyroscope alone cannot produce reliable data, and so the outputs are combined to determine accurate data for measurements such as direction, velocity, angular velocity and position. The data collected from IMU sensors may differ due to measurement errors, calibration issues, and errors due to ambient noise. Small errors in IMU sensors can cause large deviations in applications. There is no clear distinction between the performance and area of use of commercially available sensors. Therefore, when selecting a sensor, the requirements for performance should be determined for the area of use and choosen accordingly. This study investigates the performance of three IMU sensors that have no specific application area and are in common use. An experimental setup was designed and implemented to test the accuracy of the acceleration and gyroscopic information obtained from the IMU sensors. The test apparatus consists of IMU sensor, encoder, stepper motor and Raspberry Pi. The stepper motor and encoder are connected to a shaft, and the IMU sensor is mounted on a rotating moving mechanism. The apparatus is controlled by a Raspberry Pi. The Python programming language has been used for the control software. The apparatus provides rotation of a desired angle and velocity. Acceleration and gyroscopic data received from the IMU sensor are drawn in real time. All sensors were first calibrated and then data were taken. The performance of the sensors was compared using the angular values around the x-axis. The test setup was rotated at a certain angle in the x-axis using a stepper motor. The gyroscopic data on the x-axis for each IMU sensor were then read and processed through a Kalman filter. The accuracy of the IMU sensors was determined with reference to the encoder data.