Analysis of the CMU Localization Algorithm under varied conditions

Abstract

Localization is a central problem for intelligent vehicles. Visual localization can supplement or replace GPS-based localization approaches in situations where GPS is unavailable or inaccurate. Although visual localization has been demonstrated in a variety of algorithms and systems, the problem of how to best configure such a system remains largely an open question. Design choices, such as "where should the camera be placed?'" and "how should it be oriented?'' can have substantial effect on the cost and robustness of a fielded intelligent vehicle. We have previously developed a visual localization algorithm that has been analyzed with respect to seasonal variations and certain environmental conditions. This report extends this analysis with greater detail on how different sensor configuration parameters and environmental conditions affect visual localization performance with the goal of understanding what causes certain configurations to perform better than others. We ground the investigation using extensive field testing of our visual localization algorithm.