Terrain Object recognition and Context Fusion for Decision Support

Detta är en avhandling från Institutionen för datavetenskap

Sammanfattning: A laser radar can be used to generate 3D data about the terrain in a very high resolution. The development of new support technologies to analyze these data is critical to the effective and efficient use of these data in decision support systems, due to the large amounts of data that are generated. Adequate technology in this regard is currently not available and development of new methods and algorithms to this end are important goals of this work.A semi-qualitative data structure for terrain surface modelling has been developed. A categorization and triangulation process has also been developed to substitute the high resolution 3D model for this data structure. The qualitative part of the structure can be used for detection and recognition of terrain features. The quantitative part of the structure is, together with the qualitative part, used for visualization of the terrain surface. Substituting the 3D model for the semi-qualitative structures means that a data reduction is performed.A number of algorithms for detection and recognition of different terrain objects have been developed. The algorithms use the qualitative part of the previously developed semi-qualitative data structure as input. The taken approach is based on matching of symbols and syntactic pattern recognition. Results regarding the accuracy of the implemented algorithms for detection and recognition of terrain objects are visualized.A further important goal has been to develop a methodology for determining driveability using 3D-data and other geographic data. These data must be fused with vehicle data to determine the properties of the terrain context of our operations with respect to driveability. This fusion process is therefore called context fusion. The recognized terrain objects are used together with map data in this method. The uncertainty associated with the imprecision of the data has been taken into account as well.