Fusion in Signal Processing - Information Fusion in Signal and Image Processing - page 16

2.2.1. Estimation and calculation of a law a posteriori
In the context of mobile robotics, for which the general concepts of fusion will be
described in Chapter 4, the navigation of a mobile robot is a basic problem for a fusion
system. It is well-known today that the solution to this problem is obtained from the
competition between two sub-systems [ABI 92, STE 95]:
- an almost continuous navigation, using dead reckoning, based on a behavioral
model and on data provided by different proprioceptive sensors;
- a retiming operation at regular intervals based on the observation of checkpoints
or control points located near the mobile robot.
Dead reckoning uses sensors such as a gyrometer, an accelerometer, a steering
wheel angle measurements, a pedometer and an odometer (based on an angular encod-
er connected to a wheel). The exclusive use of dead reckoning works through the
integration of data using a dynamic model and cannot prevent the estimated trajec-
tory from straying from the actual trajectory. It is therefore necessary to observe the
real world at regular intervals, using sensors such as cameras, distance measurements,
acoustic or optical barriers, GPS (Global Positioning System) in order to register the
estimated trajectory with the real world. The most commonly used fusion mechanism
consists of combining various elements of information through an extended Kalman
filter that works in three phases: the first phase is a short-term prediction based on
dead reckoning navigation by proprioceptive data integration 1 ; when exteroceptive 2
data is accessible, the second phase consists of providing an estimate of its own loca-
tion based on this data; the final phase of this iterative process is a fusion categorized
as a revision or an update, which is conducted using a weighted interpolation of the
distributions between the position predicted from the proprioceptive data and the posi-
tion estimated from the exteroceptive data (see Figure 2.1, as well as section 4.2.2).
The use of an adequate Kalman filter [CHU 91] provides an optimal estimate of
the internal state involving the moving object's navigation, in the context of stochastic
dynamic systems theory [GEL 84, GOP 93]. The predicted and estimated positions
are provided by one, two or three-dimensional probability density distributions. The
greatest difficulty lies not in predicting the moving object's future position, which
can be modeled very reliably, but in the mechanism for estimating the position that
depends on the environment and the final accuracy desired. The environment can be
completely structured (in other words filled with markers leading to a precise recon-
struction of the position), partly structured (there are a certain number of markers that
can be used for regular retiming, the difficulty being to find them and use them to infer
1. Proprioceptive: able to measure an attribute involving its own state.
2. Exteroceptive: able to measure an attribute involving an external object that is present.