  Volumetric crystal structure indexing and orientation mapping are key data
processing steps for virtually any quantitative study of spatial correlations
between the local chemistry and the microstructure of a material. For electron
and X-ray diffraction methods it is possible to develop indexing tools which
compare measured and analytically computed patterns to decode the structure and
relative orientation within local regions of interest. Consequently, a number
of numerically efficient and automated software tools exist to solve the above
characterisation tasks.
  For atom probe tomography (APT) experiments, however, the strategy of making
comparisons between measured and analytically computed patterns is less robust
because many APT datasets may contain substantial noise. Given that general
enough predictive models for such noise remain elusive, crystallography tools
for APT face several limitations: Their robustness to noise, and therefore,
their capability to identify and distinguish different crystal structures and
orientation is limited. In addition, the tools are sequential and demand
substantial manual interaction. In combination, this makes robust uncertainty
quantifying with automated high-throughput studies of the latent
crystallographic information a difficult task with APT data.
  To improve the situation, we review the existent methods and discuss how they
link to those in the diffraction communities. With this we modify some of the
APT methods to yield more robust descriptors of the atomic arrangement. We
report how this enables the development of an open-source software tool for
strong-scaling and automated identifying of crystal structure and mapping
crystal orientation in nanocrystalline APT datasets with multiple phases.
