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Obstacle Detection For Thin Horizontal Structures
University of Skövde, School of Technology and Society, Skövde, Sweden.
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
2008 (English)In: World Congress on Engineering and Computer Science: WCECS 2008 : 22-24 October, 2008, San Francisco, USA, Hong Kong: International Association of Engineers, 2008, p. 689-693Conference paper, Published paper (Refereed)
Abstract [en]

Many vision-based approaches for obstacle detection often state that vertical thin structure is of importance, e.g. poles and trees. However, there are also problem in detecting thin horizontal structures. In an industrial case there are horizontal objects, e.g. cables and fork lifts, and slanting objects, e.g. ladders, that also has to be detected. This paper focuses on the problem to detect thin horizontal structures. The system uses three cameras, situated as a horizontal pair and a vertical pair, which makes it possible to also detect thin horizontal structures. A comparison between a sparse disparity map based on edges and a dense disparity map with a column and row filter is made. Both methods use the Sum of Absolute Difference to compute the disparity maps. Special interest has been in scenes with thin horizontal objects. Tests show that the sparse dense method based on the Canny edge detector works better for the environments we have tested.

Place, publisher, year, edition, pages
Hong Kong: International Association of Engineers, 2008. p. 689-693
Series
Lecture Notes in Engineering and Computer Science, ISSN 2078-0958
Keywords [en]
Computer vision, Obstacle detection, Stereo vision, Thin structures
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:hh:diva-2706ISI: 000263417100129Local ID: 2082/3108ISBN: 978-988-98671-0-2 OAI: oai:DiVA.org:hh-2706DiVA, id: diva2:239924
Conference
World Congress on Engineering and Computer Science, 22-24 October, 2008, San Francisco, USA
Available from: 2009-07-06 Created: 2009-07-06 Last updated: 2018-03-23Bibliographically approved
In thesis
1. Obstacle Detection for Driverless Trucks in Industrial Environments
Open this publication in new window or tab >>Obstacle Detection for Driverless Trucks in Industrial Environments
2014 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

With an increased demand on productivity and safety in industry, new issues in terms of automated material handling arise. This results in industries not having a homogenous fleet of trucks and driven and driverless trucks are mixed in a dynamic environment. Driven trucks are more flexible than driverless trucks, but are also involved in more accidents. A transition from driven to driverless trucks can increase safety, but also productivity in terms of fewer accidents and more accurate delivery. Hence, reliable and standardized solutions that avoid accidents are important to achieve high productivity and safety. There are two different safety standards for driverless trucks for Europe (EN1525) and U.S. (B56.5–2012) and they have developed differently. In terms of obstacles, they both consider contact with humans. However, a machinery-shaped object has recently been added to the U.S. standard (B56.5–2012). The U.S. standard also considers different materials for different sensors and non-contact sensors. For obstacle detection, the historical contact-sensitive mechanical bumpers as well as the traditional laser scanner used today both have limitations – they do not detect hanging objects. In this work we have identified several thin objects that are of interest in an industrial environment. A test apparatus with a thin structure is introduced for a more uniform way to evaluate sensors. To detect thin obstacles, we used a standard setup of a stereo system and developed this further to a trinocular system (a stereo system with three cameras). We also propose a method to evaluate 3D sensors based on the information from a 2D range sensor. The 3D model is created by measuring the position of a reflector with known position to an object with a known size. The trinocular system, a 3D TOF camera and a Kinect sensor are evaluated with this method. The results showed that the method can be used to evaluate sensors. It also showed that 3D sensor systems have potential to be used on driverless trucks to detect obstacles, initially as a complement to existing safety classed sensors. To improve safety and productivity, there is a need for harmonization of the European and the U.S. safety standards. Furthermore, parallel development of sensor systems and standards is needed to make use of state-of-the-art technology for sensors.

Place, publisher, year, edition, pages
Halmstad: Halmstad University Press, 2014. p. ix, 80
Series
Halmstad University Dissertations ; 7
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-25349 (URN)978-91-87045-14-1 (ISBN)978-91-87045-13-4 (ISBN)
Presentation
2014-09-10, Haldasalen, Visionen, Halmstad University, Kristian IV:s väg 3, Halmstad, 13:15 (English)
Opponent
Supervisors
Available from: 2014-06-05 Created: 2014-05-14 Last updated: 2018-03-22Bibliographically approved

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Hedenberg, KlasÅstrand, Björn

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CiteExportLink to record
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Citation style
  • apa
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