Team Localization: A Maximum Likelihood Approach
Andrew Howard, Maja J. Mataric and Gaurav S. Sukhatme
This paper describes a method for localizing the members of a mobile robot team by using only the robots themselves as landmarks. That is, we describe a method whereby each robot can determine the relative range, bearing and orientation of every other robot in the team, without the use of GPS, external landmarks, or instrumentation of the environment. We assume that robots are equipped with proprioceptive motion sensor (such as odometry or inertial measurement units) and some form of sensor that will allow them to make occasional measurements of the relative pose and identity of nearby robots (such sensors can be constructed using cameras or scanning laser range-finders). By employing a combination of maximum likelihood estimation and numerical optimization, we can subsequently infer the relative pose of every robot in the team at any given point in time.
This paper describes the basic formalism, its practical implementation, and presents experimental results obtained using both real and simulated robots in both static and dynamic environments.