Image Processing Reference
In-Depth Information
camera
acoustic seat belt
range finder
velocimetry
optical barriers
fusion, revision,
updating
landmark
position predicted
from an observation
updated final position
position predicted from
internal data with a possible
drift
actual
trajectory
inertial measurement unit
accelerometer
gyrometer: turning rate
pedometer
odometer
expected
initial
position
Figure 2.1.
Navigation and localization
the vehicle's position) or non-structured. The final accuracy depends on the number
of markers and on their respective configurations [BON 96, ROM 98].
The dual problem is the tracking of maneuvering moving objects that are co-
operative or non-co-operative (Figure 2.2). In a tracking system [APP 98, BAR 88,
BAR 93], the proprioceptive data from different moving objects maneuvering inside
the scene is not available. The dead reckoning navigational sub-system is then replaced
by a predictor, based on an evolutionary model that makes it possible to estimate
the position of the moving object between two consecutive observations. The mov-
ing objects in question possess maneuvering capabilities and the difficulty lies in the
choice of the adequate evolutionary model at a given time. The different sensors can
also be located in remote sites, causing the data to be out of synchronization. We then
have to define a mechanism that allows for the data to be synchronized within a grain
of time that also has to be defined.
The process then works as follows: the data is acquired at each site, a local pre-
processing phase sorts and validates the data before it is sent to the decision center,
where each valid element of data is translated in a centralized co-ordinate system and
associated with a track. At this stage of the process, we are dealing with an iterative
mechanism identical to the one we saw in navigation, a prediction phase based on a
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