Improved orientation estimation in complex environments using low-cost inertial sensors

Localization in outdoor environments is widespread, providing improved functionality and benefits for many applications. Extending this ability to in-building environments is a critical prerequisite for many location aware services. Low cost inertial sensors have the potential to improve the performance of new and existing localization systems. A challenging problem in inertial navigation is the accurate tracking of orientation. In this paper we propose a new orientation tracking algorithm that uses a complementary Kalman filter (CKF) for obtaining a reference orientation. A feedback loop is developed, which estimates the gyro bias from the CKF reference. The final orientation is the direct integration of the gyro, after removing the estimated bias. Experimental results show that the proposed algorithm provides significantly improved orientation estimates in the presence of magnetic anomalies and sensor movement, as well as increased stability and reduced noise compared the original CKF algorithm. Position calculations, using a zero-velocity update process, demonstrate the advantages of the enhanced orientation estimate as clear improvements in consequent localization.