Short Term Tracking Of Orientation With Inertial Sensors

In the past several years, IMU's have been widely used to measure the orientation of a moving body over a continuous period of time. Although, inertial navigation is a common approach for estimating the orientation, it greatly suffers from the accumulation of error in the orientation estimation. Most of the current common practices apply zero velocity update as a calibration method to address this problem and improve the estimation accuracy. However, this approach requires the sensors to be stationary frequently. This thesis introduces a novel method of calibration for estimating the elevation and bank angles of the orientation over a persistent human movement utilizing accelerometers and gyroscopes. The proposed technique incorporates the prior knowledge about the human motion to the estimation of the orientation to prevent the estimated position from growing unboundedly. The measurement model is designed to estimate the position for T seconds in the future. The knowledge of the estimated position for few seconds further in the future provides a feedback for orientation estimation during the periods of time when the accelerometer's readings are significantly deviated from gravity. This work evaluates the performance of the proposed method in two different ways: 1. a model of human movement is designed to generate synthetic data which resembles human motion. 2. an experimental design is implemented using a robot arm and an actual IMU to capture real data. The performance of the new technique is compared with the results from the inertial navigation approach. It is demonstrated that the new method significantly improves the accuracy of the orientation estimation.