Overview Recent years have seen an increasing interest in the development of multi-sensory robot systems. The reason for this interest stems from a realization that there are fundamental limitations on the reconstruction of environment descriptions using only a single source of sensor information. If robot systems are ever to achieve a degree of intelligence and autonomy, they must be capable of using many different sources of sensory information in an active and dynamic manner. The observations made by the different sensors of a multi-sensor system are always uncertain, usually partial, occasionally spuri9us or incorrect and often geographically or geometrically imcomparable with other sensor views. The sensors of these systems are characterized by the diversity of information that they can provide and by the complexity of their operation. It is the goal of a multi� sensor system to combine information from all these different sources into a robust and consistent description of the environment.

Contenuti:

1 Introduction.- 1.1 Sensors and Intelligent Robotics.- 1.2 Multi-Sensor Robot Systems.- 1.3 Organization of Sensor Systems.- 1.4 The Integration of Sensory Information.- 1.5 Coordination of Sensor Systems.- 1.6 Summary and Overview.- 2 Environment Models and Sensor Integration.- 2.1 Introduction.- 2.2 Geometric Environment Models.- 2.3 Uncertain Geometry.- 2.4 Characterizing Uncertain Geometry.- 2.4.1 Stochastic Geometry.- 2.4.2 Well-Condition Stochastic Geometries.- 2.4.3 Stochastic Topology.- 2.5 Manipulating Geometric Uncertainty.- 2.5.1 Transforming Probability.- 2.5.2 Approximate Transforms.- 2.5.3 Properties of Transformations.- 2.6 Gaussian Geometry.- 2.6.1 Changing Locations.- 2.6.2 Changing Feature Descriptions.- 2.7 Gaussian Topology.- 2.8 Summary.- 3 Sensors and Sensor Models.- 3.1 Introduction.- 3.2 Characterizing Sensors.- 3.3 Multi-Sensor System Models.- 3.4 Sensors as Team Members.- 3.5 Observation Models.- 3.6 Dependency Models.- 3.7 State Models.- 3.8 Summary.- 4 Integrating Sensor Observations.- 4.1 Introduction.- 4.2 Decision Models and Information Fusion.- 4.2.1 Decision Theory Preliminaries.- 4.2.2 Robust Decision Procedures.- 4.2.3 Observation Fusion.- 4.2.4 Sparse Data Fusion.- 4.3 Integrating Observations with Constraints.- 4.3.1 Updating a Constrained Random Network.- 4.3.2 The Three-Node Example.- 4.4 Estimating Environment Changes.- 4.4.1 Changes in Location.- 4.4.2 Using Feature Observations to Update Centroid Locations.- 4.4.3 Logical Relations and Updates.- 4.5 Consistent Integration of Geometric Observations.- 4.5.1 The Consistency Problem.- 4.5.2 Consistent Changes in Location.- 4.5.3 Consistent Updating of a Location Network.- 4.5.4 Computational Considerations.- 4.6 Summary.- 5 Coordination and Control.- 5.1 Introduction.- 5.2 The Team Decision Problem.- 5.2.1 The Structure of a Team.- 5.2.2 The Multi-Bayesian Team.- 5.2.3 Opinion Pools.- 5.3 Multi-Sensor Teams.- 5.3.1 Known and Unknown Environments.- 5.3.2 Hypothesis Generation and Verification.- 5.3.3 Constraint and Coordination.- 5.4 Sensor Control.- 5.5 Summary.- 6 Implementation and Results.- 6.1 Introduction.- 6.2 A Structure for Multi-Sensor Systems.- 6.3 Experimental Scope.- 6.4 Implementation.- 6.4.1 The Coordinator.- 6.4.2 The Agents.- 6.4.3 Communication.- 6.5 Simulation Results.- 6.6 Experimental Results.- 6.7 Summary and Conclusions.- 7 Conclusions.- 7.1 Summary Discussion.

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