POSITION AND ORIENTATION DATA REQUIREMENTS FOR PRECISE 
AUTONOMOUS VEHICLE NAVIGATION 
Louis Nastro 
Director, Land Products Applanix Corporation 85 Leek Crescent Richmond Hill, Ontario CANADA L4B3B3 
lnastro@applanix.com 
KEYWORDS: Position and Orientation, DARPA Urban Grand Challenge, Autonomous Vehicles, Sensor Fusion, POS LV 
ABSTRACT 
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. Pre 
planning 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. 
1. INTRODUCTION 
This paper addresses the problem of how to achieve reliable and 
repeatable positioning data and maximizing the performance of 
autonomous vehicles. Robust positioning (which is the ability 
of a positioning system to maintain accurate position and 
orientation information even during GPS outages), is a 
necessary component of successfully navigating the vehicle. 
However, accurate orientation of the vehicle to derive very 
precise measures of vehicle dynamics for both pre-planning 
functions and real time navigation are absolutely essential to 
provide onboard sensors with relevant data to steer autonomous 
vehicles on their intended track, and deal with unanticipated 
conditions upon routes. 
2. POS LV DESCRIPTION 
The POS LV system is a tightly coupled inertial/GPS system 
which is shown in Figure 1. Tightly-coupled implementation 
optimally blends the inertial data with raw GPS observables 
from individual satellites (ranges and range rates). In this case if 
the number of visible satellites drops below four, the inertial 
navigator is still aided by the GPS. The result is improved 
navigational accuracy when compared to free-inertial operation. 
An additional advantage of tightly-coupled integration is the 
improved re-acquisition time to recover full RTK position 
accuracy after satellite signal loss (see [1]). The inherent 
benefits of tightly-coupled data blending become readily 
apparent in the accuracy and integrity of the resulting 
navigation solution. By contrast, loosely-coupled 
implementation blends the inertial navigation data with the 
position and velocity output from the GPS. If the number of 
visible satellites is sufficient for the GPS to compute its position 
and velocity, i.e. four or more satellites, then GPS position and 
velocity are blended with the inertial data. Otherwise, if the 
GPS data is not available, the system will operate without any 
GPS aiding. The inertial navigator computes position, velocity 
and orientation of the IMU. The Kalman filter estimates the 
errors in the inertial navigator along with IMU, distance 
Figure 1: POS LV Tightly Coupled System Architecture 
measurements instrument (DMI) and GPS receivers. System 
components are shown in Figure 2. The only addition to this 
system setup for the Carnegie Mellon Red Team at the 2005 
DARPA Grand Challenge was a Trimble Ag 252 receiver which 
provided OmniSTAR VBS corrections for position information. 
Typical position accuracies for open sky conditions are in the 
order of 0.5m RMS. For the DARPA Urban Grand Challenge 
Ag 332 units were utilized and Teams had a choice to complete 
the course with OmniSTAR XP or HP corrections in order to 
achieve, in open sky conditions, 10 to 20 centimeter accuracy. 
Figure 2: POS LV System Components 
The GPS Azimuth Measurement Subsystem (GAMS) integrates 
the IMU with a 2-antenna heading measurement system. As 
long as there is GPS coverage GAMS continuously calibrates 
the IMU and azimuth does not drift. A single-antenna