# Can neural network probability directly be used as inverse sensor model?

For my robot i'm using semantic segmentation neural network that assigns to every pixel probability of being "road" (not occupied). By using homography matrix i'm re-projecting image to top-down view. The final goal is to build a map (poses are known).

I'm going to use simple algorithm "occupancy grid mapping" described in "Probabilistic Robotics" chapter 9.2. Core component of algorithm is "inverse sensor model" $$p(m_i |z_t,x_t)$$ - probability for cell $$m_i$$ being occupied given measurement $$z_t$$ and robot pose $$x_t$$

Will it be correct if i directly use probabilities from neural network as "inverse sensor model"?

if cell is outside current "top-down":
return probability_0.5
else
return probability_from_network