Performance Comparison of EKF-Based Algorithms for Orientation Estimation on Android Platform


Consumer electronics mobile devices, such as smartphones or tablets, are rapidly growing in computing power and are equipped with an increasing number of sensors. This enables to use a gift-day mobile device as a viable platform for computation-intensive, real-time applications in navigation and steering. In this paper, we have a tendency to gift a study on the performance of the orientation estimation based on the info acquired by the accelerometer, magnetometer, and gyroscope in a mobile device. Reliable orientation estimation based mostly on the readouts from inertial sensors might be utilized in additional complicated systems, e.g., to correct the orientation error of a visual odometry system. We gift a rigorous derivation of the mathematical estimation model, and we tend to thoroughly evaluate the performance of the orientation estimation mechanism on the market within the Android OS, and also the proposed alternative solutions on an distinctive dataset gathered using an actual smartphone. From the experimental results, we draw the conclusions as to the most effective performing algorithm, and then we have a tendency to evaluate its execution time on Android-primarily based devices to demonstrate the likelihood of real-time usage. The Android code for the proposed orientation estimation system is made publicly accessible for scientific and industrial applications.

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