A new sensor model which calculates the observation likelihood in a probabilistic manner 

A new likelihood model where the observation likelihoods are calculated in a probabilistic manner (joint likelihood is taken) 
New Model Type : LASER_MODEL_LIKELIHOOD_FIELD_PROB

This results in better/faster convergence of robot's pose to the correct location. (we are working on generating numbers for comparison).

Also includes the option to do beam-skipping (to better handle observations from dynamic obstacles)
Beam skipping will prevent incorporating beam likelihoods if for a majority of particles that beam doesn't agree with the map. This should prevent the pf from getting confused when the world doesn't look close to the map (e.g., when people are walking around the robot). Only applies this when the pf has converged. 

Enable beam skipping by setting do_beamskip= true