The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part BI. Beijing 2008 
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configuration, in comparison, requires dynamic heading 
alignment and delivers heading measurements that suffer from 
drift and the rate of drift is heavily dependent on vehicle 
dynamics. GAMS uses a carrier phase differential GPS 
algorithm to measure the relative position vector between the 
two antennas. These carrier phase measurements from five or 
more satellites are used to estimate, and eventually, to identify a 
set of integer phase ambiguities for each satellite being tracked 
by both receivers. For the ambiguity resolution algorithm to 
work, both receivers must track at least five common satellites. 
Once tracking has been obtained, GAMS will continue to 
operate with as few as four satellites. The GAMS heading 
system will not provide measurements when fewer than 4 GPS 
satellites are available. During GPS outages, POS LV will 
continue to provide accurate heading measurements drifting at 
the rate of about 1 arc min/min. Accurate heading is critical for 
robotic vehicle navigation especially when intermittent or non 
existent GPS conditions occur over extended periods of time. 
The distance measurement instrument (DM1) is another 
essential piece of the POS LV hardware which outputs pulses 
representing fractional revolutions of the instrumented wheel. 
These pulses are converted by the POS LV into measurements 
of incremental distance travelled by the vehicle when no GPS is 
available. In the 2005 DARPA Grand Challenge both Red 
Team vehicles Highlander and Sandstorm utilized DMI data 
not only to bridge GPS outages and provide POS LV with 
incremental distance estimation, but as an input into the velocity 
controller for detection of when the vehicle may be stuck. 
Wheel slippage is monitored by comparing the DMI output to 
the velocity reported by the POS LV system. When the system 
reports speeds over 5m/sec and a velocity of 0 m/sec., the 
vehicles execute a set of protocols utilizing the perception 
system and POS LV data to find an alternate path to the next 
pre-programmed point. 
3. THE 2005 DARPA GRAND CHALLENGE - A 
CHALLENGE IN SENSOR FUSION 
For the 2005 DARPA Grand Challenge Applanix collaborated 
with Carnegie Mellon’s Red Team for the position and 
orientation component of their two entries into the race, 
Highlander and Sandstorm [2]. Both vehicles utilized a series 
of LIDAR and radar systems to sense terrain and feed that 
information into onboard computers which would modify pre 
planned route information to avoid obstacles and deal with 
changes in terrain. The data provided by the POS LV is 
Figure 3: Sandstorm (left) and Highlander (right) were 
developed to navigate at high-speed in desert terrain. 
essential in governing vehicle dynamics to safely navigate the 
course for real time operation. The Red Team’s approach 
involved a methodical analysis of the course terrain and 
modification of the RDDF (the DARPA defined route definition 
file) in order to provide both vehicles the optimum path. 
LIDAR data (provided through a gimbal located on the roof of 
the vehicle which provided medium and long range terrain data) 
and supplemental lasers (scanning the boundaries of the 
navigable track) in addition to the short range radar (vital for 
detecting targets in the immediate vicinity of the vehicle), were 
incorporated to form a view of the world within which the 
robots would sense and evaluate terrain. Position information 
from the POS LV is critical in determining the direction of 
rotation of the gimbal in order to sense the oncoming terrain and 
georeferencing point cloud data [3]. 
The Red Team utilized a path-centric architecture which 
provided a simple method for incorporating a pre-planned route. 
The primary reason in utilizing this approach was to reduce the 
search space for a planning algorithm from the square of the 
path length to linear in the path length, since planning is 
performed in a corridor around the pre-planned route. The path 
centric approach avoided problems with arc-based arbitration 
such as discontinuities in steering commands (due to 
contradictory information) and jerky control (due to discrete 
arc-sets) [4]. 
With data derived from the LIDAR and radar systems, it is 
fused into a composite model of the terrain as illustrated in 
figure 4. The data is processed and is assigned a value dictating 
its ‘cost’. Lower elevations (shown in green) are assigned a 
lower cost whereas higher elevation (shown in red) is assigned a 
higher cost. The autonomous vehicle is ‘trained’ to navigate on 
the low cost sections of terrain and make modifications to its 
pre programmed route in the event obstacles or terrain 
anomalies block its intended path. Accurate position and 
orientation estimation is essential to this process. Map fusion is 
critical to the robustness of the navigation process, as it enables 
the system to cope with sensor failures and missing data. In 
addition to this, deriving data from multiple sources compares 
sensor input to account for anomalies. If a sensor is damaged 
and not providing accurate data, the processing algorithms will 
accord that sensor input a lower degree of confidence and adjust 
its contribution to the overall weighting of the data 
characterization and mapping process. 
Figure 4: An example cost map showing low and high cost 
terrain. 
Errors in terrain characterization can, in most cases, be 
attributed to errors not in the data acquired by the sensor, but by 
errors in position and orientation estimation. As demonstrated 
in Figure 6a which shows test data from Stanford University’s 
2005 entry ‘Stanley’, inaccurate pose can cause the vehicle to 
stop (Figure 6b) as all oncoming terrain will be perceived as not 
being traversable. The illustrations mark red terrain as not 
traversable, white is low cost terrain and grey areas are not 
known. The blue corridor is the DARPA assigned route. This 
pose error of less than 0.5 degrees in roll/pitch is enough to 
force the vehicle off the course if ignored [5]. In tests carried 
out by the Team, referenced terrain was erroneously classified