Particle filtering is a popular approach to solving estimation problems that include non-linear, multi-modal, or other irregular structures in the estimation problem. Practically, however, some combinations of problems and implementations of the particle filter require a computationally unreasonable number of particles to achieve accurate estimation results. This is especially true as the number of dimensions in the state space increases. In this paper, we investigate one particular situation where a large number of particles may be required, the kidnapped robot problem. We implement several variants of the particle filter, evaluating which ones can best localize the robot after a "kidnapping" event without requiring too many particles to be practical. We find that significant improvements in performance are available using "particle flow" particle filter implementations.
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