Model-based multiple-sensor dynamic scene interpretation

1992 ◽  
Author(s):  
Robert J. Schalkoff ◽  
Xiaoming Wang
Keyword(s):  
Dynamic Scene ◽  
Scene Interpretation ◽  
Model Based ◽  
Multiple Sensor

Nonlinear Averaging of Multi-Sensor Data

2003 ◽  
Author(s):  
Sugathevan Suranthiran ◽  
Suhada Jayasuriya
Keyword(s):  
Prior Knowledge ◽  
Sensor Fusion ◽  
Kalman Filtering ◽  
Sensor Data ◽  
Simulation Data ◽  
Model Based ◽  
Multiple Sensor ◽  
Multiple Measurements ◽  
Reliable Solution ◽  
Estimation Models

Considered in this paper is a framework for combining multiple sensor data to obtain a single inference. The task of fusing multi-sensor data is very challenging when no information about the sensor or estimation models is available. Kalman Filtering and other model-based techniques cannot be used to obtain a reliable inference. Linear Averaging of data is probably the simplest technique available, however, there is no guarantee that the fused measurement is, in fact, the best estimation. The problem will be worsened if one or more sensor measurements are faulty. In this paper, we analyze this problem and propose an effective multi-sensor fusion methodology. It is shown that a reliable solution can be obtained by nonlinearly averaging the multiple measurements. The proposed technique is well suited to identify outliers in the sensor measurements as well as to detect faulty sensor measurements. The developed algorithm is versatile in the sense that prior knowledge or information about sensors can be easily incorporated to improve the accuracy further. Illustrative examples and simulation data are presented to validate the proposed scheme.

A Purely Symbolic Model for Dynamic Scene Interpretation

1997 ◽  
Vol 06 (04) ◽  
pp. 635-664 ◽  
Author(s):  
Laurent Chaudron ◽  
Corine Cossart ◽  
Nicolas Maille ◽  
Catherine Tessier
Keyword(s):  
Petri Nets ◽  
Real World ◽  
Lattice Theory ◽  
Sensor Data ◽  
Dynamic Scene ◽  
Real World Data ◽  
Symbolic Model ◽  
Numerical Processing ◽  
Scene Interpretation ◽  
Interpretation System

The symbolic level of a dynamic scene interpretation system is presented. This symbolic level is based on plan prototypes represented by Petri nets whose interpretation is expressed thanks to 1st order cubes, and on a reasoning aiming at instantiating the plan prototypes with objects delivered by the numerical processing of sensor data. A purely symbolic meta-structure, grounded on the lattice theory, is then proposed to deal with the symbolic uncertainty issues. Examples on real world data are given.

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