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Obstacle Detection for Driverless Trucks in Industrial Environments
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. University of Skövde, Skövde, Sweden.
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. , ix, 80 p.
Series
Halmstad University Dissertations, 7
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-25349ISBN: 978-91-87045-14-1 ISBN: 978-91-87045-13-4 OAI: oai:DiVA.org:hh-25349DiVA: diva2:717327
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: 2014-06-24Bibliographically approved
List of papers
1. Stereo vision-based collision avoidance
Open this publication in new window or tab >>Stereo vision-based collision avoidance
2004 (English)In: The 9th Mechatronics Forum International Conference: Conference Proceedings, Ankara: Atılım University , 2004, 259-270 p.Conference paper, (Refereed)
Abstract [en]

This paper investigates whether a stereo vision system based on points of interest is robust enough to detect obstacles for applications like a mobile robot in an industrial environment and for the visually impaired. Points of interest are extracted with a known method, called KLT. Two algorithms to solve the correspondence problem (Sum of Squared Difference and Variance Normalized Correlation) are used and evaluated as well as a combination of the two. An improvement is made if the two algorithms are combined. The tests show that stereo vision based on points of interest only can be used robustly for obstacle detection if there is enough texture on the obstacle. Otherwise too few points of interest on the object are detected and a reliable estimation of the distance to the object cannot be made.

Place, publisher, year, edition, pages
Ankara: Atılım University, 2004
Series
Atılım University Publications, 20
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-25344 (URN)9756707135 (ISBN)9789756707135 (ISBN)
Conference
Mechatronics 2004, 9th Mechatronics Forum International Conference, Ankara, Turkey, Aug. 30 – Sep. 1, 2004
Available from: 2014-05-14 Created: 2014-05-14 Last updated: 2014-06-05Bibliographically approved
2. Obstacle Detection For Thin Horizontal Structures
Open this publication in new window or tab >>Obstacle Detection For Thin Horizontal Structures
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, 689-693 p.Conference 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
Series
Lecture Notes in Engineering and Computer Science, ISSN 2078-0958
Keyword
Computer vision, Obstacle detection, Stereo vision, Thin structures
National Category
Engineering and Technology
Identifiers
urn:nbn:se:hh:diva-2706 (URN)000263417100129 ()2082/3108 (Local ID)978-988-98671-0-2 (ISBN)2082/3108 (Archive number)2082/3108 (OAI)
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: 2014-06-05Bibliographically approved
3. A Trinocular Stereo System for Detection of Thin Horizontal Structures
Open this publication in new window or tab >>A Trinocular Stereo System for Detection of Thin Horizontal Structures
2008 (English)In: Advances in Electrical and Electronics Engineering: IAENG Special Edition of the World Congress on Engineering and Computer Science 2008, WCECS '08 / [ed] Sio-Iong Ao, Los Alamitos: IEEE Computer Society, 2008, 211-218 p.Conference 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. We introduce a test apparatus for testing thin objects as a complement for the test pieces for human safety described in the European standard EN 1525 safety of industrial trucks - driverless trucks and their systems. 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 sparse disparity map based on edges and a dense disparity map is used to identify problems with a trinocular system. Both methods use the sum of absolute difference to compute the disparity maps. Tests show that the proposed trinocular system detects all objects at the test apparatus. If a sparse or dense method is used is not critical. Further work will implement the algorithm in real time and verify it on a final system in many types of scenery.

Place, publisher, year, edition, pages
Los Alamitos: IEEE Computer Society, 2008
Keyword
European standard EN 1525 safety, absolute difference, obstacle detection, thin horizontal structures, trinocular stereo system, vertical thin structure, automatic guided vehicles, collision avoidance, stereo image processing
National Category
Computer Science
Identifiers
urn:nbn:se:hh:diva-14702 (URN)10.1109/WCECS.2008.33 (DOI)000275915300025 ()2-s2.0-70350528785 (Scopus ID)978-076953555-5 (ISBN)
Conference
World Congress on Engineering and Computer Science 2008, WCECS '08
Available from: 2011-04-02 Created: 2011-04-02 Last updated: 2014-09-24Bibliographically approved
4. Safety standard for mobile robots: a proposal for 3D sensors
Open this publication in new window or tab >>Safety standard for mobile robots: a proposal for 3D sensors
2011 (English)In: Proceedings of  the 5th European Conference on Mobile Robots, ECMR'2011 / [ed] Achim J. Lilienthal, Tom Duckett, Örebro: Centre for Applied Autonomous Sensor Systems (AASS) , 2011, 245-251 p.Conference paper, (Refereed)
Abstract [en]

In this paper we present a new and uniform way of evaluate 3D sensor performance. It is rare that standardized test specifications are used in research on mobile robots. A test rig with objects in the industrial safety standard Safety of industrial trucks - driverless trucks and their systems EN1525 is extended by thin vertical and horizontal objects that represent a fork on a forklift, a ladder and a hanging cable. A comparison of atrinocular stereo vision system, a 3D TOF (Time- Of-Flight) range camera and a Kinect device is made to verify the use of the test rig. All sensors detect the objects in the safety standard EN1525. The Kinect and 3D TOF camera shows reliable results for the objects in the safety standard at distances up to 5 m. The trinocular system is the only sensor in the test that detects the thin structures. The proposed test rig can be used to evaluate sensors to detect thin structures.

Place, publisher, year, edition, pages
Örebro: Centre for Applied Autonomous Sensor Systems (AASS), 2011
Keyword
safety standard for mobile robots, 3D sensors, Trinocular vision, EN1525
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-16085 (URN)
Conference
The 5th European Conference on Mobile Robots, ECMR 2011, Örebro, Sweden, September 7-9, 2011
Available from: 2011-09-02 Created: 2011-09-02 Last updated: 2014-06-05Bibliographically approved

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