liu.seSearch for publications in DiVA
Change search
ReferencesLink to record
Permanent link

Direct link
Context Fusion for Driveability Analysis
Linköping University, Department of Computer and Information Science. Linköping University, The Institute of Technology.
FOI (Swedish Defence Research Agency), Linköping, Sweden.
FOI (Swedish Defence Research Agency), Linköping, Sweden.
2005 (English)In: Proceedings of the 8th Int. Conf. on Information Fusion, Philadelphia Pennsylvania, USA, 2005, Vol. 2Conference paper (Refereed)
Abstract [en]

Driveability analysis is a quite complex problem that for its solution depends on several factors. One of these factors concerns the type of vehicle for which a drive-way should be determined. Besides this, the terrain structure, the type of vegetation but also the ground type and its conditions play important roles. Driveability analysis will consequently include analysis of primarily geographical information and the outcome of this analysis can be used to support decision making in command and control systems. However, quite often the required geographical information is represented in a resolution that is either too low and/or is represented with a high degree of uncertainty that cannot be neglected. In this work, an approach to driveability analysis is presented in which geographical information is regarded as context information that eventually is fused to generate paths, that may be drivable for certain types of vehicles. This information is fused by means of a knowledge-based technique that determines the driveability from a set of qualitative driveability impact factors.

Place, publisher, year, edition, pages
2005. Vol. 2
National Category
Engineering and Technology
URN: urn:nbn:se:liu:diva-13311DOI: 10.1109/ICIF.2005.1592002OAI: diva2:18312
Available from: 2008-05-27 Created: 2008-05-27
In thesis
1. Terrain Object recognition and Context Fusion for Decision Support
Open this publication in new window or tab >>Terrain Object recognition and Context Fusion for Decision Support
2008 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

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.

Place, publisher, year, edition, pages
Institutionen för datavetenskap, 2008. 88 p.
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1371
nformation Fusion, Terrain Elevation Model, Driveability, Context Fusion, Terrain Object Recognition
National Category
Computer Science
urn:nbn:se:liu:diva-11926 (URN)978-91-7393-861-7 (ISBN)
2008-06-11, Alan Turing, Hus E, Campus Valla, Linköpings universitet, Linköping, 13:15 (English)
Report code: LiU-Tek-Lic-2008:29.Available from: 2008-05-27 Created: 2008-05-27 Last updated: 2009-05-05

Open Access in DiVA

No full text

Other links

Publisher's full textLink to Ph.D. thesis

Search in DiVA

By author/editor
Jungert, Erland
By organisation
Department of Computer and Information ScienceThe Institute of Technology
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 87 hits
ReferencesLink to record
Permanent link

Direct link