Occupancy Grid Mapping refers to a family of computer algorithms in probabilistic robotics for mobile robots which address the problem of generating maps from noisy and uncertain sensor measurement data, with the assumption that the robot pose is known.
Contents
The basic idea of the occupancy grid is to represent a map of the environment as an evenly spaced field of binary random variables each representing the presence of an obstacle at that location in the environment. Occupancy grid algorithms compute approximate posterior estimates for these random variables.
Algorithm outline
There are four major components of occupancy grid mapping approach. They are:
Occupancy grid mapping algorithm
The goal of an occupancy mapping algorithm is to estimate the posterior probability over maps given the data:
Occupancy grid algorithms represent the map
If we let
The standard approach, then, is to break the problem down into smaller problems of estimating
for all grid cells
Due to this factorization, a binary Bayes filter can be used to estimate the occupancy probability for each grid cell. It is common to use a log-odds representation of the probability that each grid cell is occupied.