By Hugh F. Durrant-Whyte
Overview contemporary years have obvious an expanding curiosity within the improvement of multi-sensory robotic structures. the cause of this curiosity stems from a awareness that there are basic obstacles at the reconstruction of surroundings descriptions utilizing just a unmarried resource of sensor details. If robotic platforms are ever to accomplish a level of intelligence and autonomy, they have to be ready to utilizing many alternative resources of sensory info in an energetic and dynamic demeanour. The observations made by way of the several sensors of a multi-sensor procedure are continually doubtful, often partial, sometimes spuri9us or fallacious and sometimes geographically or geometrically imcomparable with different sensor perspectives. The sensors of those platforms are characterised via the range of knowledge that they could supply and by means of the complexity in their operation. it's the target of a multi sensor process to mix info from a lot of these varied assets right into a powerful and constant description of the environment.
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Extra info for Integration, Coordination and Control of Multi-Sensor Robot Systems
It is rare that these models provide any mechanism for the representation of uncertainty in their descriptions. , and where elemental descriptions are too global to allow single-sensor decision making. Conversely, models of geometry as images; relations between small elemental image parts, and stochastic or Markovian random field models (MRF), provide a view of geome- 24 MULTI-SENSOR INTEGRATION try as an array of simple scaler components, with no attempt to provide a functional description. These image models provide a great deal of statistical micro-structure from which uncertain descriptions of the environment can be reconstructed [Terzopoulos 84, Marroquin 85].
It is this difference which makes stochastic geometry a more difficult problem than just the manipulation of random variables. The classic example of this situation is the ubiquitous Bertrands Paradox [Bertrand 07], in which the probability of a random line intersecting a circle is shown to depend on the definition of 'line' and 'circle'. Such problems have resulted in random geometry being largely neglected as a sub-discipline of random variable-theory. There have, however, been some important contributions to the subject in recent years.
The uncertain event that the sensor "sees" a specific instance of this geometric object can be described by taking the parameter vector p to be a random variable. ); /g(p). f. describes the probability or likelyhood of observing a particular instance of the associated geometric object. It should be noted that this is not a noise model. In this context, the analysis of uncertain geometry can, in theory, be reduced to a problem in the transformation and manipulation of random variables. The study of random geometry (stochastic geometry, geometric probability)!