Simultaneous Localization and Mapping (SLAM)
نویسنده
چکیده
Recall that the main difficulty with particle filtering is that with a high dimensional state variable xt, an impossibly large number of particles is needed to accurately represent P (xt|z0:t). In some filtering problems, it is possible to exploit conditional independence of components of the state variables x1:t in order to reduce the number of particles needed. In this lecture we will see examples of this technique, known as Rao-Blackwellization, applied to SLAM and visual tracking.
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