Rao-Blackwellized Particle Filtering for Mapping Dynamic Environments

Conference PaperinProceedings - IEEE International Conference on Robotics and Automation · May 2007with9 Reads
DOI: 10.1109/ROBOT.2007.364071 · Source: IEEE Xplore
Conference: Robotics and Automation, 2007 IEEE International Conference on

    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.