Position and Orientation Data Requirements for Precise Autonomous Vehicle Navigation

The challenge of navigating an autonomous vehicle over large distances was illustrated in 2005 at the DARPA Grand Challenge when 4 out of 23 Teams successfully completed a 132 mile course within a 10 hour time limit. What the Grand Challenge revealed is that one of the most critical components of a successful autonomous vehicle was the reliability of accurate pose (positioning and orientation estimation). Data from the Applanix POS LV provided critical vehicle dynamics, navigation and planning data. Preplanning information is as important as real time navigation for achieving peak performance in autonomous driving as demonstrated by the Carnegie Mellon Red Team and their approach. With the third iteration of the DARPA Grand Challenge, autonomous vehicles were required to navigate an urban course which contained dynamic obstacles a host of other impediments, providing the most realistic operational environment to date for autonomous vehicles. This paper will outline the uses of positioning and orientation data for autonomous vehicle operations at the 2005 and 2007 events and how the Applanix POS LV system was an integral part of the Tartan Racing and Stanford University top finishing results at the DARPA Urban Grand Challenge.