Visual localization based on quadtrees

Abstract

Autonomous mobile robotsmoving through their environment to perform the tasks for which they were programmed. The robot proper operation largely depends on the quality of the self localization information used when globally navigating in its environment. This paper describes a method of maintaining a self-location probability distribution of a set of states, which represents the robot position. The novel feature of this approach is to represent the state space as a Quadtree that dynamically evolves to use the minimum set of statements without loss of accuracy. We demonstrate the benefits of this approach in localizing a robot in the RoboCup SPL environment using the information provided by its camera.