Inertial Head-Tracker Sensor Fusion by a Complimentary Separate-Bias Kalman Filter
VRAIS '96 Proceedings of the 1996 Virtual Reality Annual International Symposium (VRAIS 96)
Using the marginalised particle filter for real-time visual-inertial sensor fusion
ISMAR '08 Proceedings of the 7th IEEE/ACM International Symposium on Mixed and Augmented Reality
Activity recognition using biomechanical model based pose estimation
EuroSSC'10 Proceedings of the 5th European conference on Smart sensing and context
Analyzing and evaluating markerless motion tracking using inertial sensors
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part I
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A tiny absolute orientation estimating device equipped with a network function has been developed using accelerometers and magnetometers to estimate gravity and the geomagnetic field, respectively. Because accelerometers measure motion other than gravity, a method has been proposed to eliminate the effect of motion. An estimation method is proposed that excludes the effect of magnetic disturbances. An advantage of this estimation method is that models can be switched according to the environment. Sigma Points Kalman Filters (SPKFs) were evaluated to determine the proper filter for the proposed algorithm. A Square-Root Central Difference Kalman Filter (SR-CDKF) was the best method considering stability, accuracy, and calculation cost. Using simulations and the realized device, the proposed algorithm stably estimated the orientation in the presence of motion and magnetic disturbances.