Planned perception within concurrent mapping and localization

Abstract

The fundamental requirement of truly autonomous mobile robots is navigation. Navigation is the science of determining one's position and orientation based on information provided by various sensors. Mobile robot navigation, especially autonomous vehicle navigation, is confronted with the problem of attempting to determine the structure of an a priori unknown environment, while at the same time using this information for navigation purposes. This problem is referred to as concurrent mapping and localization (CML). This thesis addresses the question of how to improve CML performance through smarter sensing strategies affecting robot behavior. Planned perception is the process of adaptively determining the sensing strategy of the mobile robot. The goal of integrating planned perception within concurrent mapping and localization is to attempt to answer the question of how a mobile robot should behave so as to attempt to optimize CML performance. This thesis demonstrates in simulation how the CML framework could be improved with planned perception by motivating changes in robot pose and hence, sensing locale.