Rao-Blackwellized Particle Filtering for Mapping Dynamic Environments
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.
- Journal of Field Robotics 07/2008; 25(8):493 - 527. · 2.15 Impact Factor
- PACT 2009, Proceedings of the 18th International Conference on Parallel Architectures and Compilation Techniques, 12-16 September 2009, Raleigh, North Carolina, USA; 01/2009