I've recently come across the concept of using information gain (or mutual information criteria) as a metric for minimizing entropy on a map to aid in robotic exploration. I have somewhat of a basic question about it.
A lot of papers that talk about minimizing entropy consider an example case of something like a laser scanner and try to compute the 'next best pose' so that the maximum entropy reduction is achieved. Usually this is mentioned like "information gain based approaches help finding the best spot to move the robot such that the most entropy is minimized using raycasting techniques, as opposed to frontier based exploration which is greedy" etc. But I don't understand what the underlying reason is for information gain/entropy based exploration being better.
Let's say a robot in a room with three walls and open space in front. Because of range limitations, it can only see two walls: so in frontier based exploration, the robot has two choices; move towards the third wall and realize it's an obstacle, or move towards the open space and keep going. How does an information gain based method magically pick the open space frontier over the wall frontier? When we have no idea what's beyond our frontiers, how can raycasting even help?