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This paper introduces a new attitude estimation algorithm for pitch and roll angles. Pitch and roll angles are represented by a unit vector, and its estimation error is estimated in the Kalman filter. The main theoretical contribution is that the error covariance equations are simplified to scalar equations. Thus, the proposed algorithm is computationally efficient. The proposed algorithm is also applied to vertical movement estimation. Simulation and experiment results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Position and orientation system (POS) is widely applied to the various mapping sensors for airborne direct georeferencing. As the accuracy requirements vary in different mapping applications, the POS error analysis is important for the system design. In this paper, an analytical approach to the POS error analysis for ground alignment and constant-velocity flight is proposed. First, the simplified system model is obtained for the error analysis. Then, by applying the two-stage Kalman filter, the analytical relations between the attitude errors and estimation errors of the biases of the inertial sensors are derived. Finally, the experiment is carried out to confirm the effectiveness of the proposed method. The proposed method is not only effective and straightforward but also provides us some new insights into the POS error characteristics. [ABSTRACT FROM AUTHOR]

This paper proposes a Kalman filter-based attitude (i.e., roll and pitch) estimation algorithm using an inertial sensor composed of a triaxial accelerometer and a triaxial gyroscope. In particular, the proposed algorithm has been developed for accurate attitude estimation during dynamic conditions, in which external acceleration is present. Although external acceleration is the main source of the attitude estimation error and despite the need for its accurate estimation in many applications, this problem that can be critical for the attitude estimation has not been addressed explicitly in the literature. Accordingly, this paper addresses the combined estimation problem of the attitude and external acceleration. Experimental tests were conducted to verify the performance of the proposed algorithm in various dynamic condition settings and to provide further insight into the variations in the estimation accuracy. Furthermore, two different approaches for dealing with the estimation problem during dynamic conditions were compared, i.e., threshold-based switching approach versus acceleration model-based approach. Based on an external acceleration model, the proposed algorithm was capable of estimating accurate attitudes and external accelerations for short accelerated periods, showing its high effectiveness during short-term fast dynamic conditions. Contrariwise, when the testing condition involved prolonged high external accelerations, the proposed algorithm exhibited gradually increasing errors. However, as soon as the condition returned to static or quasi-static conditions, the algorithm was able to stabilize the estimation error, regaining its high estimation accuracy. [ABSTRACT FROM AUTHOR]