  Occupancy grids encode for hot spots on a map that is represented by a two
dimensional grid of disjoint cells. The problem is to recursively update the
probability that each cell in the grid is occupied, based on a sequence of
sensor measurements from a moving platform. In this paper, we provide a new
Bayesian framework for generating these probabilities that does not assume
statistical independence between the occupancy state of grid cells. This
approach is made analytically tractable through the use of binary asymmetric
channel models that capture the errors associated with observing the occupancy
state of a grid cell. Binary-valued measurement vectors are the thresholded
output of a sensor in a radar, sonar, or other sensory system. We compare the
performance of the proposed framework to that of the classical formulation for
occupancy grids. The results show that the proposed framework identifies
occupancy grids with lower false alarm and miss detection rates, and requires
fewer observations of the surrounding area, to generate an accurate estimate of
occupancy probabilities when compared to conventional formulations.
