figure 6. Course map provided by DARPA, here shown with our data
figure 8. Lateral localization in Junior, based on infrared remission
values acquired by the laser. the yellow graph depicts the posterior
lateral position estimate.
visible in the laser scans to map features, to further refine
Stanley’s localization only addresses the lateral location of the robot relative to the map. Figure 7 illustrates the
analysis of the terrain for a discrete set of vertical offsets.
Localization then adjusts the estimated INS pose estimates
such that the center line of the road in the map aligns with
the center of the drivable corridor. As a result, Stanley tends
to stay centered on the road (unless, of course, the robot
swerves to avoid an obstacle).
Junior’s localization is essentially identical, but using
infrared remission values of the laser in addition to range-based obstacle features. Infrared remission facilitates
figure 7. Localization uses momentary sensor data to estimate the
location of the robot relative to the map with centimeter precision.
in Stanley, the localization only estimated the lateral location, as
indicated by the lateral offset bars.
102 CommuniCAtionS of thE ACm | aPrIL 2010 | VoL. 53 | no. 4
the detection of lane markings, which are not detectable
with range. Figure 8 illustrates an infrared remission scan,
superimposed with the localization results. The yellow
curve in this figure represents the posterior distribution
over the lateral offset to the map, as estimated by fusing
INS and remission values. In this specific instance, the
localizer reduces the GPS error from about a meter to a few
3. 5. obstacle tracking
Roads are full of obstacles. Many are static, such as ruts and
berms in the desert, or curbs and parked vehicles in an urban
environment. To avoid such static obstacles, both vehicles
build local occupancy grid maps8 that maintain the location
of static obstacles. Figure 9 shows examples of a maps built
by both robots. Whereas Stanley distinguished only three
types of terrain—drivable, occupied, and unexplored—
Junior also categorizes the type of obstacles by height, which
leads to an approximate distinction of curbs, cars, and tall
trees, as illustrated in Figure 9b.
Equally relevant is the tracking of moving objects such
as cars, which play a major role in urban driving. The key
element of detecting moving objects is temporal differencing. If two subsequent laser scans mark a region as free in
one scan, and occupied in another, then this joint observation constitutes a potential “witness” of a moving object.
For the situation depicted in Figure 10a, Figure 10b illustrates such an analysis. Here scan points colored red or
green correspond to such witnesses. The set of witnesses
is then filtered (e.g., points outside the drivable map are
removed) and moving objects are then tracked using particle filters. Figure 10c depicts an example result in which
Junior finds and tracks four vehicles.
Further vehicle tracking is provided using radar sensors. To this end, Junior possesses three radar detectors,
one pointing straight ahead, and two pointing to each side.
The radars provide redundancy in moving object detection, and hence enhance the vehicle’s reliability when
merging into moving traffic; however, they are only used