Improved occupancy grid learning: The conform approach to occupancy grid apping

A central requirement for the development of robotic systems, that are capable of autonomous operation in non-specific environments, is the ability to create maps of their operating locale. The creation of these maps is a non trivial process as the robot has to interpret the findings of its sensors so as to make deductions regarding the state of its environment. Current approaches fall into two broad categories: on-line and offline. An on-line approach is characterised by its ability to construct a map as the robot traverses its operating environment, however this comes at the cost of representational clarity. An offline approach on the other hand requires all sensory data to be gathered before processing begins but is capable of creating more accurate maps. In this paper we present a new means of constructing occupancy grid maps which addresses this problem.