Python: Ensure we use modules/executables from our distribution
pc_align: Dump inverse transform as well
Core: Add a test to make sure that ip are centered and extrema

The code wasn't testing for extrema and it wasn't centered correctly.
Core: Add thresholding of lower 0.1% of Interest Points

The lower percent of interest points are likely due to floating point
noise in the integral image. I discovered this in the unit test.
Core: Prevent clustering from finding null sets.

If there is just one outlier before, it would calculate a zero
variance. This is true, however on the next step that prevents
distances from being calculated. Now we prevent the variance from
going below double's epsilon.
core: Have altitude clustering happen after triangulation clustering.

Altitude's inlier cluster is assumed to be the largest cluster.
Core: IntegralAutoGainDetector reuse code from vw::IIPD

IIP == IntegralInterestPointDetector
all: Update gitignore

We've all written new tools and unit tests!
pc_align: Minor tweaks
pc_align: Minor bugfix in output file names
pc_align: Some speedup using multithreading
pc_align: Resample file while reading
Save transformed datasets wout loading fully in memory
configure: Require Boost to find libpointmatcher

Libpointmatcher links against:

yaml
nabo
boost_thread
boost_filesystem
boost_system
boost_program_options
boost_date_time
libstdc++
pc_align: Debug code
pc_align: Load more pts if filter gross outliers
pc_align: Save errors in csv
Fix doc
pc_align: Shift in respect to first point
pc_align: More memory reduction