  Keeping a vehicle well-localized within a prebuilt-map is at the core of any
autonomous vehicle navigation system. In this work, we show that both standard
SIR sampling and rejection-based optimal sampling are suitable for efficient
(10 to 20 ms) real-time pose tracking without feature detection that is using
raw point clouds from a 3D LiDAR. Motivated by the large amount of information
captured by these sensors, we perform a systematic statistical analysis of how
many points are actually required to reach an optimal ratio between efficiency
and positioning accuracy. Furthermore, initialization from adverse conditions,
e.g., poor GPS signal in urban canyons, we also identify the optimal particle
filter settings required to ensure convergence. Our findings include that a
decimation factor between 100 and 200 on incoming point clouds provides a large
savings in computational cost with a negligible loss in localization accuracy
for a VLP-16 scanner. Furthermore, an initial density of $\sim$2
particles/m$^2$ is required to achieve 100% convergence success for large-scale
($\sim$100,000 m$^2$), outdoor global localization without any additional hint
from GPS or magnetic field sensors. All implementations have been released as
open-source software.
