  We propose a method for deterministic sampling of arbitrary continuous
angular density functions. With deterministic sampling, good estimation results
can typically be achieved with much smaller numbers of samples compared to the
commonly used random sampling. While the Unscented Kalman Filter uses
deterministic sampling as well, it only takes the absolute minimum number of
samples. Our method can draw arbitrary numbers of deterministic samples and
therefore improve the quality of state estimation. Conformity between the
continuous density function (reference) and the Dirac mixture density, i.e.,
sample locations (approximation) is established by minimizing the difference of
the cumulatives of many univariate projections. In other words, we compare
cumulatives of probability densities in the Radon space.
