no_spinning option now publishes a spin speed of 0

Also changed the spindle speed printout to ROS_DEBUG so the constant
publishing doesn't clutter up the terminal.
Removed the unnecessary state_publisher node
Created node that converts between laser scan and pointcloud2

This is launched automatically by the mulitsense launch file
Forgot a temp file pattern for Emacs in the .gitignore
Created a new package that will contain the map creation code.

Currently contains a test node that publishes a checkerboard map
Can make simple 2D maps. Not using probabilities though so
pretty noisy and lots of false occupancy. Also I am not clearing spaces
which the laser beam passes though.
Marking unoccupied cells by rasterizing the beam onto the map grid.

Still not using the log odds or inverse sensor model. So the map
is noisy, but the basics are working
Created the outline of the program that will determine the sensor
model parameters using the EM algorithm from page 160
of the probabilistic robotics textbook.

Launches the robot facing a wall. The parameter learning node
gets the wall pose and saves a specified number of scans to run
through the EM algorithm.
Created a model plugin that publishes the wall info as a visualization
message. This allows it to be displayed in RVIZ and the sensor model
learner can now get the exact wall position, orientation and size
Can now set a static spin rate, tilt angle and pan angle in all
launch files
switched to using the box_info topic to get the wall pose and size
Can now calculate the intersection distance according to the map
EM seems to be working. Need to save the params to a file
and clean up the code. Some of the functionality will be useful
in the other programs using the sensor model
Moved most of the functions to a header file. Still could be cleaned up
a lot more
Separated out the em node's specific code from the more
general parameter class and functions.
Added functions that save and load the params via yaml files
Fixed a mistake in the pShort calculation of the sensor model
Added the learned sensor parameters to the multisense_sensor_model package
Now incrementing and decrementing the occupancy likelihood of each cell
to mimic the inverse sensor model described in the book. Considering
learning the inverse sensor model, but for now I'm going to stick with
just the manual approximation.
Fixed a race condition in the laser callback