Stochastic mapping frameworks

Abstract

Stochastic mapping is an approach to the concurrent mapping and localization (CML) problem. The approach is powerful because feature and robot states are explicitly correlated. Improving the esti- mate of any state automatically improves the estimates of correlated states. This paper describes a number of extensions to the stochastic mapping framework, which are made possible by the incorporation of past vehicle states into the state vector to explicitly represent the robot's trajectory. Having access to past robot states simplifles mapping, navigation, and cooperation. Ex- perimental results using sonar data are presented.