Utilization of Position and Orientation Data for Preplanning and Real Time Autonomous Vehicle Navigation

Abstract

Pre-planning information about terrain is as important as real time navigation for achieving peak performance in autonomous driving. Both of these rely on accurate, trustworthy estimates of position and orientation. Preplanning uses position-registered terrain data to detail an intended route and set desired speeds to achieve elapsed times that are otherwise impossible. Navigation and path tracking also rely on position estimation for effective driving. This paper profiles technologies that successfully guided Sandstorm and H1ghlander, two robots from the Carnegie Mellon Red Team, through a 132 mile course. Fusion of data from various onboard sensors provides accurate real time perception. To control driving, position estimation must persist even during prolonged periods of GPS outages. The application of the Applanix POS LV for both preplanning and real time operation of the vehicle is outlined to illustrate how the system provides highly accurate position and orientation which is crucial in maximizing the performance of autonomous vehicles. This paper first describes the hardware and architecture which comprises the POS LV system and how the data from the POS LV was utilized. This is followed by analysis of test results which highlight the system’s robust positioning capabilities in GPS adverse environments and demonstrates how position and orientation information is used for preplanning.