This PR adds a unit test for the 2D PnP optimization. I used this as an attempt to understand how to use Eigen objects directly in optimizations for use in the development of future cost functions (i.e. kinematic calibration). Some changes to note:

1. The cost function takes two arguments (camera to target quaternion and position), opposed to the single argument previously (camera to target position and un-normalized axis angle rotation).
2. The cost function uses the Eigen::Map class to construct an Eigen object directly from a template data pointer
3. Uses Ceres local parameterization to reduce the 4 quaternion values into the minimal number of variables for optimization (i.e. 3)
   • Supposedly you can create an SE(3) local parameterization, such that the cost function input can be a single 6 DOF pose. I wasn't able to figure out how that would work with Eigen objects yet though

Overall writing this cost function using Eigen objects was not difficult, and it makes the functions much more understandable. Also porting this change to the other cost functions would eventually allow us to phase out the custom Pose6d to reduce overall code size/maintenance.