KLD-Sampling: Adequately Sampling from an Unknown Distribution
Copyright (C) 2006 - Patrick Beeson (pbeeson at cs.utexas.edu)
This program is released under the GNU General Public License (GPL).
This code implements Dieter Fox's KLD-sampling algorithm (KLD stands
for Kullback-Leibler distance). When using particle filters to
approximate an unknown distribution, too few particles may not
adequately sample the underlying distribution, while too many samples
can increase the run time of time sensitive programs (e.g. particle
filter localization for a mobile robot). Running this program
demonstrates how different KLD-sampling parameters affect both the
number of samples and the estimated mean and variance of the
underlying distribution. This sample program assumes a 1D underlying
distribution, but the provided KLD-sampling module works on
multivariate distributions.
Relevant Citations