Comparing Estimation Algorithms for Camera Position and Orientation
State estimation deals with estimation of the state of an object of interest by observing noisy measurements. The process to obtain the state estimations is called filtering. In this report several filters are compared to an existing one. The new filters deal with nonlinear process and measurement models in a different way than the existing filter. Instead of approximating the nonlinear transformations the probability densities are approximated by a set of points which undergo the nonlinear transformation.
The application for which the filters will be used is to estimate the position and orientation of a camera in a markerless environment, using data from an inertial measurement unit and a camera. It is found that the corresponding process and measurement models contain nonlinearities and therefore an accuracy improvement is expected with the new filters.
The new filters are variations of the so-called unscented Kalman filter. Also a discussion on the marginalized particle filter is presented. Instead of using randomly chosen samples as in the particle filter methods, the unscented Kalman filter uses deterministically chosen points. The marginalized particle filter partitions the variables of the system in a linear and a nonlinear part. Linear Kalman filters are applied to the linear variables and a particle filter to the nonlinear variables, thus reducing the computational load. Details of various implementations of the filters are given, as well as the motivation for the specific implementations.
Tests are carried out to assess the performance of the filters. This is done with both simulation data and real measurements. A comparison is made to the original extended Kalman filter. The tests are focussed on accuracy and computational load.
Results showed that the use of the new filters did not improve accuracy. This is mainly due to the fact that the nonlinearities are not so severe. Furthermore the filters had a higher computational load, which is an important aspect in the system reviewed in this report. Therefore the current filter need not to be replaced. The unscented Kalman filter is a good alternative to the EKF in case of new applications, since it can handle the system in a black-box manner in contrast to the EKF.
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