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