Tightly-coupled GPS / INS system design for autonomous urban navigation

Abstract

This paper analyzes the design decisions made in building the tightly-coupled position, velocity, and attitude estimator used as a position feedback signal for autonomous navigation in Cornell University's 2007 DARPA urban challenge robot, 'Skynet.' A statistical sensitivity analysis is conducted on Skynet's estimator by examining the changes in its output as critical design decisions are reversed. The effects of five design decisions are considered: map aiding via computer vision algorithms, inclusion of differential corrections, filter integrity monitoring, WAAS augmentation, and inclusion of carrier phases. The effects of extensive signal blackouts are also considered. All estimator variants are scrutinized both in a statistical sense and in a practical sense, by comparing each variant's performance on logged data recorded at the 2007 DARPA urban challenge.