Abstract
Accurate tracking of the orientation of a rigid object is important in several domains, such as sports training, rehabilitation and animation. As technology develops, IMU-based method becomes an increasingly popular approach for navigation and motion tracking. However, IMUs suffer from integration drift. As a result, technologies for reduction of the integration drift are important and meaningful. In this paper, we gave a review on principles of IMU-based pose estimation methods, introduced an integration drift reduction method called Kalman Filter and discussed the proper application fields for wearable IMU. Compared to other sensor used for pose estimation, wearable IMU has better self-independence, leading to its validity in all the environments, and it can provide good real-time estimation at a small cost of money and power.
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