Extend likelihood field to model unexplored space

[nahueespinosa]

Description

Probabilistic Robotics, Chapter 6.4.2 mentions a few disadvantages of the likelihood field sensor model:

• It does not explicitly model people and other dynamics that might cause short readings.
• It treats sensors as if they can “see through walls".
• It does not take map uncertainty into account.

To diminish the effect of these limitations, the following extension is proposed:

[...] one might sort map occupancy values into three categories: occupied, free, and unknown, instead of just the first two. When a sensor measurement $z^k_t$ falls into the category unknown, its probability $p(z^k_t | x_t, m)$ is assumed to be the constant value $1 / z_{max}$. The resulting probabilistic model is that in the unexplored space, every sensor measurement is equally likely.

Definition of Done

• Update our likelihood field model implementation to model unexplored space.