MEMS-Based IMU for Pose Estimation
This research investigates pose estimation using micro-electro-mechanical-system (MEMS)-based inertial measurement unit (IMU) in real time. Accelerometers suffer from errors caused due to accelerations that add to gravity. This makes the position obtained from accelerometers unreliable and inaccurate. Gyroscopes are encountered by data drifting problems. This paper illustrates pose estimation by integrating the IMU data. A moving average filter is used to get the position information by reducing abrupt variations in the accelerometer data, whereas the complementary filter passes accelerometer and gyroscope data through low- and high-pass filters to obtain orientation. The results are recorded for both static and dynamic conditions which show that both complementary and moving average filters are less sensitive to variations compared to that by only integrating the IMU data.
KeywordsComplementary filter IMU Pose estimation Moving average filter Data fusion
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