Building a qualitative local occupancy grid in a new vision-based reactive navigation strategy for mobile robots

Reactive navigation strategies for mobile robots base their success on the ability to discriminate obstacles from the ground in the vicinity of the robot and many of these strategies are based uniquely on the computation of quantitative information. In this paper we describe a new method to build qualitative local occupancy grids that are used in a new vision-based navigation strategy addressed to mobile robots to explore safely unknown environments. The process includes a new feature classifier based on the inverse perspective transformation to discriminate object from ground points and a method to determine angle and range with respect to the camera in the world coordinate system.