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7.2 Sensor Fusion and 3 D Object Pose Identification 97

7.2 Sensor Fusion and 3 D Object Pose Identifi-

cation

Sensor fusion overcomes the limitation of individual sensor values for a
particular task. When one kind of sensor cannot provide all the neces-
sary information, a complementary observation from another sensory sub-
system may fill the gap. Multiple sensors can be combined to improve
measurement accuracy or confidence in recognition (see e.g. Baader 1995;
Murphy 1995). The concept of a sensor system can be generalized to a “vir-
tual sensor” – an abstract sensor module responsible for extracting certain
feature informations from one or several real sensors.
In this section we suggest the application of a PSOM to naturally solve
the sensor fusion problem. For the demonstration the previous planar
(2D) problem shall be extended.
Assume a 3 D object has a set of salient features which are observed
by one or several sensory systems. Each relevant feature is detected by a
“virtual sensor”. Depending on the object pose, relative to the observing
system, the sensory values change, and only a certain sub-set of features
may be successfully detected.
When employing a PSOM, its associative completion capabilities can
solve a number of tasks:

knowing the object pose, predict the sensor response;

knowing a sub-set of sensor values, reconstruct the object pose, and

complete further information of interest (e.g. in the context of a ma-
nipulation task pose related grasp preshape and approach path in-
formations);

generate hypotheses for further perception “schemata”, i.e. predict
not-yet-concluded sensor values for “guidance” of further virtual
sensors.

7.2.1 Reconstruct the Object Orientation and Depth

Here we want to extent the previous planar object reconstruction exam-
ple to the three-dimensional world, which gives in total to 6 degrees of