We present an autoadaptive algorithm for in-use parameter estimation of MEMS inertial accelerometers and gyros1 using multi-level quasi-static states for greater accuracy and reliability. Multi-level quasi-static states are detected robustly using data from both gyros and accelerometers. Proper estimation of time-varying sensor parameters allows us to develop a mixed-reality real-time hand-held orientation tracker with dynamic accuracy of less than 20. Existing methods like Kalman filters do not take time-varying nature of parameters into account, instead modelling the time-variation as higher values in noise covariance matrices; thus underestimating the sensor capabilities. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Saxena, A., Gupta, G., Gerasimov, V., & Ourselin, S. (2005). In use parameter estimation of inertial sensors by detecting multilevel quasi-static states. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3684 LNAI, pp. 595–601). Springer Verlag. https://doi.org/10.1007/11554028_82
Mendeley helps you to discover research relevant for your work.