Rao-Blackwellized Particle Filtering for Mapping Dynamic Environments
Isaac Miller and Mark Campbell
Abstract:
A general method for mapping dynamic environments
using a Rao-Blackwellized particle filter is presented.
The algorithm rigorously addresses both data association and
target tracking in a single unified estimator. The algorithm
relies on a Bayesian factorization to separate the posterior into
1) a data association problem solved via particle filter and
2) a tracking problem with known data associations solved
by Kalman filters developed specifically for the ground robot
environment. The algorithm is demonstrated in simulation and
validated in the real world with laser range data, showing
its practical applicability in simultaneously resolving data
association ambiguities and tracking moving objects.
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