科研之友
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摘要
We examine several algorithms for tracking a handheld wand in a 3D virtual reality system: extended Kalman filters (EKFs), interacting multiple models (IMMs), and support vector machines (SVMs). The IMMs consist of several EKF models, each of which is tuned for one particular type of user motion. For determining the types of motion, we compare hand-created rules with an automatic clustering algorithm, with mixed results. The mode-specific EKFs within the IMM are more accurate than one overall EKF. However, the IMM is comparable to a single EKF, because of the overhead of predicting the current component EKF. SVMs with a one-frame lookahead perform the best, cutting the error in half. Aside from those SVMs, different model types were best for the different dimensions of tracking (x, y, z, and rotation).
- 出版日期2014-10-21
