Initial Robust Sensor Model Implementation

Additions

Relatively optimized PDF and CDF functions for Normal Distributions.
- Please check that the implementation matches the PDF and CDF definitions in https://en.wikipedia.org/wiki/Normal_distribution.
Two new hyperparameters, kGaussianLowerBound and kGaussianUpperBound, currently both defined as 1.5 times the standard deviation of the sensor.
- These are defined as deltas because the actual interval depends on each sensor reading.

The Observation Likelihood Model now follows a piecewise PDF defined in this slide.
The Observation Likelihood Model now first computes linear-likelihoods since we need to normalize probability values with the integral of the PDF. The weight of each particle still stores the log-likelihood.
I changed the error in translation from translational movement to 1.0 temporarily. I think having a good amount of spread allows the update model to correct itself when the location estimate is really off.
I changed gamma temporarily to 0.6. This needs to be tuned.

These are only conjectures based on looking at the simulator and not hard-set rules.

Having a large particle spread is not necessarily a bad thing – it increases the probability that the particle filter will correct a location estimate that is very wrong.
When the location estimate is very close to the true location of the car, the top five log-likelihoods should all be pretty close to 0.
When the location estimate is very wrong, one (or a few) of the top five log-likelihoods should be much greater than the rest. This assumes that there exists a particle that is much more correct than the location estimate.